Fire_summary

Abstract

This webpage will show the results of a bat survey study done in the Plumas National Forest in North California. The objective of this study is to determine the distribution of the different species of bats within the park. In order to do that we have performed occupancy models for the species present in the park. The results of this models will be shown as maps showing the probability of occurence of bats in each point, that is, if you see a value of 1, there is a 100% chance of finding a bat in that point, if there is a value of 0 there is 0% chance of finding that specie in that point, if there is a value of 0.5 there is a 50% chance of finding that specie in that point.

Another result

Results collected in the field

Maps showing the sampled Points

Results of species prescence

In this area 0 means absence, and 1 means prescence. This table has for each site (ID), every specie and day, so for example if Mylu1=0, that means that for Myotis lucifugus (common name Little Brown bat, was detected on day one for that particular site).

Here is a key for bat species

• Myotis yumanensis (Myyu)
• Myotis californicus (Myca)
• Myotis ciliolabrum (Myci)
• Myotis volans (Myvo)
• Myotis lucifugus (Mylu)
• Parastrellus hesperus (Pahe)
• Lasiurus blossevillii (Labo)
• Myotis evotis (Myev)
• Antrozous pallidus (Anpa)
• Eptesicus fuscus (Epfu)
• Lasionycteris noctivagans (Lano)
• Myotis thysanodes (Myth)
• Tadarida brasiliensis (Tabr)
• Lasiurus cinereus (Laci)
• Corynorhinus townsendii (Coto)
• Euderma maculatum (Euma)
• Eumops perotis (Eupe)

Maps predicting the distribution of bats

Yuma myotis (Myotis yumanensis)

Statistical models

	Model 1	Model 2	Model 3
psi(Int)	-0.07	0.08	0.28
	(0.86)	(0.92)	(1.09)
p(Int)	-1.30*	-1.44*	-1.57*
	(0.61)	(0.61)	(0.63)
p(Mintemp)		0.41
		(0.33)
p(Meantemp)			0.40
			(0.32)
Log Likelihood	-47.31	-46.54	-46.55
AICc	98.88	99.62	99.64
Delta	0.00	0.75	0.76
Weight	0.03	0.02	0.02
Num. obs.	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

California bat (Myotis californicus)

Total model

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Western Small Footed Myotis (Myotis ciliolabrum)

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Hairy-winged bat (Myotis volans)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
psi(Int)	-0.40	-0.42	-0.29	-0.33	-0.22	-0.60	-0.65	-0.37	-0.37
	(0.62)	(0.58)	(0.67)	(0.65)	(0.69)	(0.53)	(0.53)	(0.64)	(0.63)
p(Int)	-0.98	-0.96	-1.10*	-1.05	-1.15*	-0.74	-0.66	-1.01	-1.00
	(0.54)	(0.52)	(0.55)	(0.55)	(0.54)	(0.58)	(0.64)	(0.54)	(0.54)
p(Maxtemp)		0.42
		(0.37)
p(Maxhum)			-0.32
			(0.31)
p(Meanhum)				-0.26
				(0.31)
p(sdtemp)					0.34
					(0.42)
p(Meantemp)						0.43
						(0.56)
p(Mintemp)							0.44
							(0.63)
p(Minhum)								-0.18
								(0.31)
p(sdhum)									-0.19
									(0.34)
Log Likelihood	-48.44	-47.77	-47.93	-48.09	-48.11	-48.11	-48.17	-48.26	-48.29
AICc	101.14	102.08	102.39	102.70	102.76	102.76	102.88	103.05	103.12
Delta	0.00	0.94	1.25	1.56	1.62	1.62	1.74	1.91	1.98
Weight	0.21	0.13	0.11	0.10	0.10	0.10	0.09	0.08	0.08
Num. obs.	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Little Brown bat (Myotis lucifugus)

Statistical models

	Model 1	Model 2
psi(Int)	-0.41	-0.46
	(0.33)	(0.32)
p(Int)	1.16*	1.28**
	(0.47)	(0.46)
p(Julian)	-0.72
	(0.45)
p(Maxtemp)	-8.48*	-7.45*
	(3.75)	(3.35)
p(Mintemp)	7.67*	6.93*
	(3.11)	(2.82)
p(sdtemp)	8.64*	7.79*
	(3.42)	(3.12)
Log Likelihood	-51.57	-52.91
AICc	117.14	117.21
Delta	0.00	0.07
Weight	0.51	0.49
Num. obs.	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Western Red Bat (Lasiurus blossevillii)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
psi(Int)	6.28	0.87	0.63	5.35	5.72	5.71	7.80	6.47	0.86	5.63	7.21	0.40
	(40.98)	(1.00)	(0.94)	(41.14)	(39.15)	(33.44)	(57.62)	(29.46)	(0.96)	(32.02)	(46.65)	(0.93)
p(Int)	-2.58***	-2.49***	-2.31***	-2.62***	-2.60***	-2.61***	-2.74***	-2.72***	-2.50***	-2.60***	-2.72***	-2.14***
	(0.36)	(0.54)	(0.54)	(0.42)	(0.38)	(0.38)	(0.40)	(0.40)	(0.54)	(0.38)	(0.39)	(0.58)
p(Julian)	-0.65*	-0.66		-0.68*	-0.64	-0.67*	-0.70	-0.67	-0.59	-0.66*	-0.74*
	(0.33)	(0.37)		(0.34)	(0.33)	(0.34)	(0.36)	(0.36)	(0.39)	(0.33)	(0.36)
p(Maxtemp)		-7.29*	-8.31**						-7.82*			-7.86*
		(3.20)	(3.13)						(3.06)			(3.27)
p(Mintemp)		6.27*	11.25**	0.31			0.54		10.06**		0.52	6.64*
		(2.62)	(3.87)	(0.32)			(0.35)		(3.77)		(0.35)	(2.66)
p(sdtemp)		6.28*	8.48**						7.73**			6.83*
		(2.75)	(2.90)						(2.84)			(2.78)
p(Meantemp)			-4.21			0.28		0.57	-3.40
			(2.45)			(0.33)		(0.37)	(2.40)
p(Meanhum)					0.27		0.54	0.58
					(0.31)		(0.37)	(0.39)
p(Maxhum)										0.25	0.50
										(0.32)	(0.37)
Log Likelihood	-39.25	-36.11	-36.22	-38.78	-38.87	-38.88	-37.65	-37.68	-35.02	-38.94	-37.79	-37.81
AICc	85.03	86.21	86.43	86.47	86.65	86.67	86.70	86.76	86.78	86.79	86.98	87.01
Delta	0.00	1.18	1.40	1.44	1.62	1.64	1.67	1.73	1.74	1.76	1.95	1.98
Weight	0.17	0.09	0.08	0.08	0.08	0.08	0.07	0.07	0.07	0.07	0.06	0.06
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Long-eared Bat (Myotis evotis)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10
psi(Int)	1.39**	1.39**	1.54**	1.39**	1.38**	1.41**	1.56**	1.44**	1.43**	1.40**
	(0.44)	(0.44)	(0.53)	(0.44)	(0.44)	(0.45)	(0.55)	(0.46)	(0.46)	(0.45)
p(Int)	0.36	0.37	0.28	0.38	0.38	0.36	0.27	0.34	0.33	0.34
	(0.23)	(0.23)	(0.24)	(0.23)	(0.23)	(0.23)	(0.25)	(0.23)	(0.23)	(0.23)
p(Maxhum)	-0.42*		-0.56*	-0.93		-0.40	-0.58*	-1.51
	(0.21)		(0.25)	(0.59)		(0.21)	(0.27)	(0.79)
p(Meanhum)		1.25		0.54				1.74		-0.30
		(0.80)		(0.57)				(1.07)		(0.20)
p(Minhum)		-1.93*			-0.52*			-0.77	-0.32
		(0.95)			(0.24)			(0.52)	(0.20)
p(sdhum)		-0.98*			-0.39
		(0.48)			(0.24)
p(Mintemp)			-0.28
			(0.27)
p(sdtemp)						0.19
						(0.21)
p(Meantemp)							-0.28
							(0.30)
Log Likelihood	-94.44	-92.58	-93.92	-93.96	-93.96	-94.04	-94.05	-92.82	-95.34	-95.39
AICc	195.42	196.55	196.75	196.83	196.83	196.98	197.01	197.04	197.22	197.31
Delta	0.00	1.13	1.33	1.41	1.41	1.56	1.60	1.62	1.80	1.89
Weight	0.19	0.11	0.10	0.09	0.09	0.09	0.09	0.09	0.08	0.07
Num. obs.	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Pallid Bat (Antrozous pallidus)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
psi(Int)	-1.40**	-1.32**	-1.29**	-1.23*	-1.38**	-1.37**	-1.26*
	(0.44)	(0.48)	(0.50)	(0.53)	(0.45)	(0.46)	(0.53)
p(Int)	-0.20	-0.37	-0.42	-0.54	-0.23	-0.28	-0.50
	(0.52)	(0.58)	(0.61)	(0.65)	(0.53)	(0.55)	(0.68)
p(Meanhum)		0.35
		(0.43)
p(Julian)			-0.35
			(0.46)
p(Mintemp)				-0.51
				(0.69)
p(Minhum)					0.31
					(0.51)
p(Maxhum)						0.24
						(0.39)
p(Meantemp)							-0.41
							(0.64)
Log Likelihood	-37.22	-36.89	-36.95	-37.02	-37.03	-37.03	-37.06
AICc	78.70	80.32	80.44	80.57	80.59	80.60	80.65
Delta	0.00	1.61	1.73	1.87	1.89	1.90	1.95
Weight	0.29	0.13	0.12	0.11	0.11	0.11	0.11
Num. obs.	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Fringed Bat (Myotis thysanoides)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
psi(Int)	-0.43	-0.71	-0.74	-0.90*	-0.83	-0.89	-1.06*	-0.59	-0.74	-0.81	-0.81	-0.72
	(0.72)	(0.50)	(0.50)	(0.45)	(0.47)	(0.46)	(0.51)	(0.61)	(0.51)	(0.49)	(0.44)	(0.50)
p(Int)	-1.48*	-1.32*	-1.25*	-1.11	-1.22*	-1.06	-0.64	-1.39*	-1.37*	-1.27*	-1.30*	-1.29*
	(0.66)	(0.58)	(0.58)	(0.63)	(0.61)	(0.62)	(0.55)	(0.61)	(0.62)	(0.62)	(0.59)	(0.59)
p(Julian)	0.82	1.36*	1.25*	1.85*	1.37*	1.51*		1.11	1.46*	1.38*	1.60*	1.29*
	(0.42)	(0.66)	(0.59)	(0.84)	(0.67)	(0.68)		(0.58)	(0.72)	(0.67)	(0.75)	(0.61)
p(Meanhum)		0.87		1.99					1.26		3.60*	1.62
		(0.63)		(1.13)					(0.81)		(1.77)	(1.19)
p(Maxhum)			0.83		1.47	1.62				1.21
			(0.59)		(0.82)	(0.91)				(0.77)
p(Maxtemp)				1.70		1.32
				(1.24)		(1.05)
p(Meantemp)					1.15				0.77		1.54
					(0.92)				(0.87)		(0.93)
p(Minhum)								0.54			-2.17	-0.93
								(0.63)			(1.44)	(1.22)
p(Mintemp)										0.75
										(0.84)
Log Likelihood	-38.31	-37.13	-37.16	-36.18	-36.41	-36.42	-40.05	-37.83	-36.67	-36.71	-35.54	-36.84
AICc	83.14	83.17	83.23	83.76	84.22	84.24	84.35	84.56	84.73	84.81	85.08	85.09
Delta	0.00	0.02	0.08	0.61	1.08	1.09	1.21	1.41	1.58	1.66	1.93	1.94
Weight	0.13	0.13	0.13	0.10	0.08	0.08	0.07	0.07	0.06	0.06	0.05	0.05
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Townsend’s Long-eared Bat (Corynorhinus townsendii)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13
psi(Int)	-1.36*	-1.55***	-1.35*	-1.39**	-1.64***	-1.61***	-1.53**	-1.65***	-1.39**	-1.62***	-1.60***	-1.45**	-1.36*
	(0.55)	(0.44)	(0.53)	(0.50)	(0.42)	(0.42)	(0.47)	(0.42)	(0.53)	(0.43)	(0.43)	(0.53)	(0.55)
p(Int)	-0.68	-0.11	-0.73	-0.57	0.40	1.27	-0.28	0.69	-0.61	0.28	0.17	-0.52	-0.68
	(0.64)	(0.64)	(0.62)	(0.60)	(0.63)	(0.88)	(0.69)	(0.72)	(0.63)	(0.71)	(0.75)	(0.68)	(0.64)
p(sdhum)		1.25	0.59
		(0.76)	(0.53)
p(sdtemp)		-1.63			-1.83	-4.08	-0.67			-2.17	-2.08
		(1.01)			(1.07)	(2.12)	(0.79)			(1.22)	(1.26)
p(Julian)				0.64		-1.59
				(0.56)		(1.05)
p(Minhum)					-1.01	-2.46		-1.38		-2.78	-2.04	-0.24
					(0.67)	(1.31)		(0.82)		(1.61)	(1.17)	(0.44)
p(Meantemp)								-6.66*					0.21
								(3.30)					(0.39)
p(Mintemp)								6.49*	0.31
								(3.15)	(0.40)
p(Meanhum)										2.03
										(1.53)
p(Maxhum)											1.52
											(1.16)
Log Likelihood	-33.00	-30.85	-32.30	-32.32	-31.25	-30.01	-32.65	-30.25	-32.69	-30.33	-30.40	-32.85	-32.85
AICc	70.26	70.61	71.13	71.17	71.40	71.42	71.84	71.89	71.92	72.06	72.20	72.22	72.23
Delta	0.00	0.35	0.87	0.92	1.15	1.16	1.58	1.64	1.67	1.80	1.95	1.97	1.97
Weight	0.14	0.12	0.09	0.09	0.08	0.08	0.06	0.06	0.06	0.06	0.05	0.05	0.05
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

The western pipistrelle (Parastrellus hesperus)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
psi(Int)	-2.07**	-1.96**	-2.38**	-2.51***	-1.94*	-1.95*	-1.96**	-2.56***	-1.97**	-1.97*	-1.97**	-2.02**
	(0.75)	(0.76)	(0.73)	(0.76)	(0.75)	(0.76)	(0.76)	(0.74)	(0.76)	(0.77)	(0.76)	(0.75)
p(Int)	-40.03	-72.83	-3.17	7.25	-16.15	-18.59	-47.00	5.15	-23.44	-25.79	-34.78	-18.73
	(41.99)	(78.15)	(8.92)	(11.20)	(15.77)	(27.96)	(54.69)	(7.06)	(32.54)	(37.52)	(32.67)	(21.66)
p(Julian)	103.25	112.56	35.03	69.48	31.13	41.56	79.48	53.28	48.64	44.87	59.41	38.15
	(120.80)	(118.24)	(49.25)	(123.97)	(50.57)	(55.42)	(90.19)	(58.78)	(58.73)	(58.93)	(53.81)	(39.79)
p(Meantemp)	29.31				42.83	9.83				15.87		11.82
	(31.42)				(85.37)	(14.82)				(21.88)		(13.41)
p(Maxtemp)		51.39					35.95
		(54.74)					(41.41)
p(sdtemp)			34.74			20.66			27.54
			(49.99)			(31.13)			(34.72)
p(Meanhum)				-40.50							-20.77	-11.63
				(65.65)							(19.56)	(17.58)
p(Mintemp)					-34.83				12.76		22.26
					(68.45)				(17.13)		(20.68)
p(sdhum)							-4.58
							(8.16)
p(Maxhum)								-35.64		-12.71
								(37.76)		(19.61)
Log Likelihood	-6.45	-6.48	-6.92	-7.15	-6.04	-6.06	-6.08	-7.36	-6.13	-6.14	-6.17	-6.20
AICc	21.81	21.87	22.75	23.22	23.47	23.52	23.55	23.63	23.66	23.67	23.73	23.80
Delta	0.00	0.06	0.95	1.41	1.66	1.71	1.74	1.82	1.85	1.86	1.93	2.00
Weight	0.16	0.15	0.10	0.08	0.07	0.07	0.07	0.06	0.06	0.06	0.06	0.06
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

big brown bat (Eptesicus fuscus)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18	Model 19	Model 20
psi(Int)	-0.36	-0.36	-0.36	-0.26	-0.13	-0.15	-0.27	-0.25	-0.13	-0.05	-0.26	-0.16	-0.25	-0.13	-0.28	-0.26	-0.29	-0.05	-0.36	-0.30
	(0.33)	(0.33)	(0.33)	(0.37)	(0.42)	(0.40)	(0.35)	(0.37)	(0.40)	(0.41)	(0.37)	(0.42)	(0.37)	(0.40)	(0.36)	(0.37)	(0.35)	(0.43)	(0.32)	(0.35)
p(Int)	0.21	0.19	0.21	-0.05	-0.26	-0.21	0.07	-0.07	-0.21	-0.33	-0.04	-0.20	-0.03	-0.21	-0.01	-0.02	0.06	-0.34	0.20	0.11
	(0.35)	(0.34)	(0.35)	(0.41)	(0.43)	(0.41)	(0.37)	(0.41)	(0.39)	(0.40)	(0.40)	(0.46)	(0.42)	(0.40)	(0.41)	(0.44)	(0.37)	(0.40)	(0.34)	(0.37)
p(Maxhum)	1.65											1.37	1.35			1.32				1.26
	(1.05)											(1.02)	(1.04)			(1.06)				(1.11)
p(Meanhum)	-2.22*		-0.65*	-0.79*		-0.92*	-0.61*	-0.86*	-0.75*	-0.87*		-2.19*	-2.04*	-0.81*		-2.07*			-0.65*	-1.83
	(1.06)		(0.30)	(0.34)		(0.37)	(0.30)	(0.35)	(0.32)	(0.36)		(1.03)	(1.04)	(0.35)		(1.05)			(0.30)	(1.13)
p(Minhum)		-0.70*			-0.98*						-0.77*				-0.80*		-0.61	-0.89*
		(0.32)			(0.39)						(0.33)				(0.34)		(0.34)	(0.39)
p(Mintemp)				-0.62					-0.63		-0.53		-0.53
				(0.41)					(0.39)		(0.40)		(0.44)
p(Maxtemp)					-0.87	-0.84				-0.83		-0.78						-0.82
					(0.52)	(0.50)				(0.49)		(0.56)						(0.49)
p(Julian)							0.53		0.54	0.52				0.52			0.38	0.37		0.35
							(0.38)		(0.36)	(0.35)				(0.36)			(0.41)	(0.36)		(0.43)
p(Meantemp)								-0.61						-0.60	-0.43	-0.48
								(0.42)						(0.41)	(0.42)	(0.48)
p(sdhum)																			0.29
																			(0.34)
Log Likelihood	-63.21	-64.43	-64.51	-63.37	-63.38	-63.46	-63.56	-63.56	-62.34	-62.34	-63.60	-62.48	-62.50	-62.60	-63.95	-62.74	-64.01	-62.88	-64.13	-62.90
AICc	135.33	135.39	135.55	135.65	135.66	135.83	136.03	136.03	136.08	136.08	136.12	136.36	136.40	136.59	136.80	136.87	136.94	137.15	137.17	137.20
Delta	0.00	0.06	0.22	0.33	0.34	0.50	0.70	0.70	0.75	0.75	0.79	1.03	1.07	1.26	1.47	1.54	1.61	1.83	1.84	1.87
Weight	0.08	0.07	0.07	0.06	0.06	0.06	0.05	0.05	0.05	0.05	0.05	0.05	0.04	0.04	0.04	0.04	0.03	0.03	0.03	0.03
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

silver-haired bat (Lasionycteris noctivagans)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
psi(Int)	0.13	0.15	0.13	0.19	0.21	0.12	0.10	0.14	0.13
	(0.30)	(0.31)	(0.30)	(0.34)	(0.35)	(0.30)	(0.30)	(0.31)	(0.31)
p(Int)	0.65*	0.63*	0.64*	0.49	0.46	0.67*	0.75*	0.62*	0.63*
	(0.28)	(0.28)	(0.28)	(0.35)	(0.39)	(0.28)	(0.31)	(0.28)	(0.28)
p(Maxtemp)		-0.35
		(0.28)
p(Minhum)			0.20
			(0.24)
p(Meantemp)				-0.29
				(0.37)
p(Mintemp)					-0.29
					(0.38)
p(sdhum)						-0.19
						(0.25)
p(sdtemp)							-0.20
							(0.28)
p(Meanhum)								0.16
								(0.23)
p(Maxhum)									0.13
									(0.25)
Log Likelihood	-78.54	-77.78	-78.17	-78.22	-78.23	-78.24	-78.28	-78.30	-78.39
AICc	161.34	162.09	162.87	162.98	162.99	163.02	163.10	163.13	163.32
Delta	0.00	0.75	1.54	1.64	1.65	1.68	1.76	1.79	1.98
Weight	0.21	0.15	0.10	0.09	0.09	0.09	0.09	0.09	0.08
Num. obs.	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14
psi(Int)	0.17	0.16	0.15	0.13	-0.58	-0.80	-0.23	0.20	-0.22	-0.61	0.20	0.15	0.18	0.15
	(0.33)	(0.33)	(0.32)	(0.32)	(0.60)	(0.67)	(0.45)	(0.35)	(0.46)	(0.53)	(0.35)	(0.33)	(0.34)	(0.33)
psi(Burn.intensity.basal)	0.72							1.04				0.46
	(0.37)							(0.65)				(0.48)
p(Int)	0.65*	0.65*	0.65*	0.65*	0.63*	0.62*	0.64*	0.65*	0.64*	0.64*	0.64*	0.65*	0.65*	0.65*
	(0.28)	(0.28)	(0.28)	(0.28)	(0.29)	(0.30)	(0.28)	(0.28)	(0.28)	(0.28)	(0.28)	(0.28)	(0.28)	(0.28)
psi(Burn.intensity.Canopy)		0.71									1.04			0.43
		(0.36)									(0.69)			(0.49)
psi(Burn.intensity.soil)			0.68*										1.11
			(0.34)										(0.71)
psi(fire_dist)				-0.65*	-0.61	-1.70	-0.57	-1.46	-0.58		-1.41	-0.35	-1.35	-0.35
				(0.32)	(0.33)	(1.04)	(0.33)	(0.99)	(0.32)		(0.98)	(0.44)	(0.97)	(0.45)
psi(I(Burn.intensity.soil^2))					0.81	1.08				0.79
					(0.69)	(0.83)				(0.50)
psi(forest_dist)						-1.22		-1.63			-1.61		-1.69
						(1.04)		(1.15)			(1.15)		(1.19)
psi(I(Burn.intensity.basal^2))							0.41
							(0.41)
psi(I(Burn.intensity.Canopy^2))									0.40
									(0.43)
Log Likelihood	-76.15	-76.23	-76.27	-76.32	-75.16	-74.22	-75.67	-74.44	-75.74	-76.95	-74.58	-75.84	-74.65	-75.92
AICc	158.84	159.00	159.07	159.18	159.23	159.83	160.24	160.28	160.38	160.43	160.55	160.58	160.70	160.76
Delta	0.00	0.16	0.23	0.35	0.40	1.00	1.41	1.44	1.55	1.59	1.72	1.74	1.86	1.92
Weight	0.12	0.11	0.10	0.10	0.10	0.07	0.06	0.06	0.05	0.05	0.05	0.05	0.05	0.04
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Brazilian free-tailed bat (Tadarida brasiliensis)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18	Model 19
psi(Int)	1.91*	1.66**	1.78*	1.36**	1.68**	1.72*	1.65**	1.62**	1.94*	1.30**	1.78*	1.60**	1.47**	1.35**	1.92*	1.69*	1.73*	1.31**	1.62**
	(0.81)	(0.64)	(0.71)	(0.45)	(0.64)	(0.67)	(0.62)	(0.60)	(0.85)	(0.42)	(0.73)	(0.61)	(0.49)	(0.44)	(0.84)	(0.66)	(0.68)	(0.42)	(0.61)
p(Int)	0.15	0.22	0.19	0.38	0.23	0.21	0.23	0.25	0.14	0.40	0.20	0.24	0.33	0.40	0.15	0.23	0.22	0.41	0.26
	(0.25)	(0.25)	(0.25)	(0.23)	(0.25)	(0.25)	(0.26)	(0.25)	(0.26)	(0.23)	(0.26)	(0.26)	(0.24)	(0.24)	(0.26)	(0.26)	(0.26)	(0.23)	(0.26)
p(Maxtemp)	0.67					0.36		-0.45							0.65		0.34		-0.47
	(0.36)					(0.27)		(0.29)							(0.38)		(0.27)		(0.30)
p(Meantemp)	-0.98*		-0.43								-0.43	-0.38			-0.96*
	(0.40)		(0.24)								(0.25)	(0.24)			(0.42)
p(Mintemp)		-0.45			-0.39	-0.69*	-0.45		-0.56*							-0.41	-0.69*
		(0.23)			(0.24)	(0.30)	(0.24)		(0.25)							(0.25)	(0.32)
p(sdtemp)			0.42	0.38	0.31			0.69*			0.42		0.46	0.35		0.29			0.69*
			(0.23)	(0.22)	(0.23)			(0.31)			(0.24)		(0.24)	(0.23)		(0.23)			(0.32)
p(Julian)							-0.28				-0.23			-0.26	-0.20	-0.26	-0.25	-0.30	-0.27
							(0.21)				(0.21)			(0.22)	(0.21)	(0.21)	(0.21)	(0.21)	(0.22)
p(Meanhum)									1.84*				0.26
									(0.84)				(0.21)
p(Minhum)									-2.22*
									(0.96)
p(sdhum)									-0.93*
									(0.47)
Log Likelihood	-92.53	-93.76	-92.58	-94.00	-92.83	-92.84	-92.89	-92.90	-90.63	-95.52	-91.96	-94.40	-93.28	-93.28	-92.06	-92.09	-92.11	-94.56	-92.14
AICc	193.97	194.05	194.08	194.53	194.57	194.59	194.69	194.71	195.26	195.31	195.32	195.33	195.47	195.47	195.52	195.57	195.61	195.65	195.67
Delta	0.00	0.08	0.10	0.56	0.60	0.62	0.71	0.74	1.29	1.33	1.34	1.35	1.49	1.50	1.55	1.60	1.64	1.67	1.70
Weight	0.09	0.08	0.08	0.06	0.06	0.06	0.06	0.06	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04
Num. obs.	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

hoary bat (Lasiurus cinereus)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10
psi(Int)	0.40	0.56	0.65	0.46	0.57	0.65	0.46	0.43	0.52	0.60
	(0.34)	(0.41)	(0.43)	(0.36)	(0.38)	(0.44)	(0.36)	(0.35)	(0.36)	(0.42)
p(Int)	0.28	0.09	0.02	0.21	0.13	0.01	0.20	0.24	0.23	0.06
	(0.27)	(0.30)	(0.30)	(0.28)	(0.29)	(0.31)	(0.29)	(0.28)	(0.29)	(0.31)
p(Julian)		-0.39	-0.42		-0.53*	-0.40			-0.56*	-0.38
		(0.26)	(0.25)		(0.26)	(0.25)			(0.27)	(0.26)
p(Maxhum)			0.35	0.31	1.66				3.82*
			(0.23)	(0.25)	(1.00)				(1.90)
p(Meanhum)					-1.28	0.25	0.23		-3.36
					(0.96)	(0.22)	(0.23)		(1.83)
p(Minhum)								0.19		0.18
								(0.23)		(0.22)
p(sdhum)									-0.70
									(0.52)
Log Likelihood	-84.33	-83.27	-82.15	-83.54	-81.25	-82.63	-83.82	-83.97	-80.32	-82.94
AICc	172.93	173.07	173.21	173.62	173.90	174.17	174.17	174.47	174.65	174.79
Delta	0.00	0.14	0.28	0.69	0.97	1.24	1.24	1.54	1.72	1.86
Weight	0.15	0.14	0.13	0.11	0.10	0.08	0.08	0.07	0.07	0.06
Num. obs.	49	49	49	49	49	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Spotted bat (Euderma maculatum)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5
psi(Int)	6.01	7.72	6.82	6.64	7.66
	(48.06)	(83.65)	(51.51)	(51.34)	(78.44)
p(Int)	-4.48***	-4.19***	-3.96***	-4.66***	-4.39***
	(1.01)	(0.84)	(0.70)	(1.18)	(0.94)
p(Minhum)	-1.73			-2.06
	(1.02)			(1.23)
p(Meanhum)		-1.24			-1.26
		(0.74)			(0.77)
p(Maxhum)			-0.92
			(0.55)
p(sdhum)				-0.39
				(0.53)
p(sdtemp)					0.59
					(0.54)
Log Likelihood	-16.03	-16.35	-16.74	-15.70	-15.77
AICc	38.59	39.24	40.02	40.31	40.44
Delta	0.00	0.65	1.43	1.72	1.85
Weight	0.33	0.24	0.16	0.14	0.13
Num. obs.	49	49	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

western mastiff bat (Eumops perotis)

Statistical models

	Model 1	Model 2	Model 3
psi(Int)	-1.18	-1.04	-1.03
	(1.07)	(1.04)	(1.10)
p(Int)	-1.54	-1.76	-1.76
	(1.04)	(0.98)	(1.04)
p(Mintemp)		-0.57
		(0.62)
p(Meantemp)			-0.47
			(0.52)
Log Likelihood	-24.15	-23.69	-23.73
AICc	52.56	53.90	53.99
Delta	0.00	1.35	1.44
Weight	0.50	0.26	0.24
Num. obs.	49	49	49
p < 0.001, p < 0.01, p < 0.05

doing row 1000 of 108500 doing row 2000 of 108500 doing row 3000 of 108500 doing row 4000 of 108500 doing row 5000 of 108500 doing row 6000 of 108500 doing row 7000 of 108500 doing row 8000 of 108500 doing row 9000 of 108500 doing row 10000 of 108500 doing row 11000 of 108500 doing row 12000 of 108500 doing row 13000 of 108500 doing row 14000 of 108500 doing row 15000 of 108500 doing row 16000 of 108500 doing row 17000 of 108500 doing row 18000 of 108500 doing row 19000 of 108500 doing row 20000 of 108500 doing row 21000 of 108500 doing row 22000 of 108500 doing row 23000 of 108500 doing row 24000 of 108500 doing row 25000 of 108500 doing row 26000 of 108500 doing row 27000 of 108500 doing row 28000 of 108500 doing row 29000 of 108500 doing row 30000 of 108500 doing row 31000 of 108500 doing row 32000 of 108500 doing row 33000 of 108500 doing row 34000 of 108500 doing row 35000 of 108500 doing row 36000 of 108500 doing row 37000 of 108500 doing row 38000 of 108500 doing row 39000 of 108500 doing row 40000 of 108500 doing row 41000 of 108500 doing row 42000 of 108500 doing row 43000 of 108500 doing row 44000 of 108500 doing row 45000 of 108500 doing row 46000 of 108500 doing row 47000 of 108500 doing row 48000 of 108500 doing row 49000 of 108500 doing row 50000 of 108500 doing row 51000 of 108500 doing row 52000 of 108500 doing row 53000 of 108500 doing row 54000 of 108500 doing row 55000 of 108500 doing row 56000 of 108500 doing row 57000 of 108500 doing row 58000 of 108500 doing row 59000 of 108500 doing row 60000 of 108500 doing row 61000 of 108500 doing row 62000 of 108500 doing row 63000 of 108500 doing row 64000 of 108500 doing row 65000 of 108500 doing row 66000 of 108500 doing row 67000 of 108500 doing row 68000 of 108500 doing row 69000 of 108500 doing row 70000 of 108500 doing row 71000 of 108500 doing row 72000 of 108500 doing row 73000 of 108500 doing row 74000 of 108500 doing row 75000 of 108500 doing row 76000 of 108500 doing row 77000 of 108500 doing row 78000 of 108500 doing row 79000 of 108500 doing row 80000 of 108500 doing row 81000 of 108500 doing row 82000 of 108500 doing row 83000 of 108500 doing row 84000 of 108500 doing row 85000 of 108500 doing row 86000 of 108500 doing row 87000 of 108500 doing row 88000 of 108500 doing row 89000 of 108500 doing row 90000 of 108500 doing row 91000 of 108500 doing row 92000 of 108500 doing row 93000 of 108500 doing row 94000 of 108500 doing row 95000 of 108500 doing row 96000 of 108500 doing row 97000 of 108500 doing row 98000 of 108500 doing row 99000 of 108500 doing row 100000 of 108500 doing row 101000 of 108500 doing row 102000 of 108500 doing row 103000 of 108500 doing row 104000 of 108500 doing row 105000 of 108500 doing row 106000 of 108500 doing row 107000 of 108500 doing row 108000 of 108500

Relationships between different species of Bats

Fire bats

MYYU	MYCA	MICI	MYVO	MYLU	LABL	MYEV	ANPA	MYTH	COTO	PAHE	EPFU	LANO	TABR	LACI	EUMA	EUPE
0.1746977	0.6187110	0.0523755	0.4020923	0.1814072	1.0000000	0.6074240	0.1495259	0.2619755	0.0551907	0.0000000	0.1056724	0.3976295	0.6932020	0.5987302	0.0002035	0.0010027
1.0000000	0.6476518	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.1417360	0.0000000	0.2467996	0.0000262	0.8744714	0.4065435	0.7122902	0.5987302	1.0000000	1.0000000
0.1746977	0.6187110	0.0543530	0.4020923	0.1854588	1.0000000	0.6155645	0.1495259	0.2619755	0.0551907	0.0000000	0.1129229	0.3976295	0.6932020	0.5987302	0.0004526	0.0010027
0.1746977	0.6187110	0.0836853	0.4020923	0.2386986	1.0000000	0.7060096	0.1495259	0.2619755	0.0551907	0.0000000	0.2350532	0.3976295	0.6932020	0.5987302	0.8526811	0.0010027
1.0000000	0.9005471	0.4422312	0.4020923	0.5826017	0.0009523	0.9409064	0.2914249	1.0000000	0.3854519	0.3559797	0.7230384	0.7541266	0.9900645	0.5987302	1.0000000	1.0000000
0.3635506	0.6200561	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.1488006	0.5059076	0.0551907	0.0000000	0.9470735	0.3984167	0.6949216	0.5987302	1.0000000	0.0000199
0.9999994	0.9146396	0.4422312	0.4020923	0.5826017	0.9977640	0.9409064	0.4115917	0.9998384	0.3599207	0.0938242	0.8931081	0.7844351	0.9934588	0.5987302	1.0000000	1.0000000
0.1746977	0.6187110	0.0518274	0.4020923	0.1802711	1.0000000	0.6051050	0.1495259	0.2619755	0.0551907	0.0000000	0.1036929	0.3976295	0.6932020	0.5987302	0.0001623	0.0010027
0.1746977	0.6187110	0.0731587	0.4020923	0.2208694	1.0000000	0.6788340	0.1495259	0.2619755	0.0551907	0.0000000	0.1888414	0.3976295	0.6932020	0.5987302	0.2274919	0.0010027
1.0000000	0.8397064	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0967128	1.0000000	0.2754353	0.0002558	0.2403815	0.5645056	0.9226269	0.5987302	0.0000000	0.0000002
0.1746977	0.6187110	0.0751250	0.4020923	0.2242944	1.0000000	0.6842758	0.1495259	0.2619755	0.0551907	0.0000000	0.1973338	0.3976295	0.6932020	0.5987302	0.3457951	0.0010027
0.1746977	0.6187110	0.0658899	0.4020923	0.2077879	1.0000000	0.6570073	0.1495259	0.2619755	0.0551907	0.0000000	0.1582155	0.3976295	0.6932020	0.5987302	0.0287791	0.0010027
0.1746977	0.6187110	0.0641996	0.4020923	0.2046441	1.0000000	0.6515040	0.1495259	0.2619755	0.0551907	0.0000000	0.1512973	0.3976295	0.6932020	0.5987302	0.0165205	0.0010027
0.1746977	0.6187110	0.0444506	0.4020923	0.1643850	1.0000000	0.5709207	0.1495259	0.2619755	0.0551907	0.0000000	0.0784362	0.3976295	0.6932020	0.5987302	0.0000060	0.0010027
0.1746977	0.6187110	0.0676257	0.4020923	0.2109749	1.0000000	0.6624818	0.1495259	0.2619755	0.0551907	0.0000000	0.1654060	0.3976295	0.6932020	0.5987302	0.0497388	0.0010027
1.0000000	0.8973325	0.4422312	0.4020923	0.5826017	0.1515941	0.9409064	0.3385960	1.0000000	0.2869110	0.0006107	0.6782625	0.7675747	0.9917059	0.5987302	1.0000000	1.0000000
1.0000000	0.9048351	0.4422312	0.4020923	0.5826017	0.9723134	0.9409064	0.3876448	0.9999995	0.4365624	0.9338712	0.7810998	0.7793024	0.9929586	0.5987302	1.0000000	1.0000000
1.0000000	0.8349757	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1012291	1.0000000	0.2753032	0.0002532	0.2317935	0.5922719	0.9404390	0.5987302	0.0337443	0.0000000
0.1746977	0.6187110	0.0818469	0.4020923	0.2356713	1.0000000	0.7015910	0.1495259	0.2619755	0.0551907	0.0000000	0.2268686	0.3976295	0.6932020	0.5987302	0.7792485	0.0010027
0.1746977	0.6187110	0.0836827	0.4020923	0.2386943	1.0000000	0.7060034	0.1495259	0.2619755	0.0551907	0.0000000	0.2350415	0.3976295	0.6932020	0.5987302	0.8525931	0.0010027
0.1746977	0.6187110	0.0863798	0.4020923	0.2430748	1.0000000	0.7122625	0.1495259	0.2619755	0.0551907	0.0000000	0.2471107	0.3976295	0.6932020	0.5987302	0.9215102	0.0010027
1.0000000	0.7085289	0.4422312	0.4020923	0.5826017	0.9999994	0.9409064	0.1277455	1.0000000	0.0551907	0.0000000	0.5497607	0.4258041	0.7506282	0.5987302	0.9997273	0.0000000
1.0000000	0.8311463	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0997119	1.0000000	0.3768551	0.2401771	0.2264190	0.5851544	0.9362483	0.5987302	0.0000037	0.0004466
0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.7039228	0.0000493	0.3871204	0.3810857	0.9578770	0.8334665	0.9970076	0.5987302	1.0000000	1.0000000
1.0000000	0.7879798	0.4422312	0.4020923	0.5826017	0.3523369	0.9409064	0.1073739	1.0000000	0.2981489	0.0014033	0.2370232	0.4685517	0.8217859	0.5987302	0.0000000	0.0000000
1.0000000	0.7960210	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1095783	1.0000000	0.2753889	0.0002549	0.2265655	0.6192454	0.9542550	0.5987302	0.0000000	0.0000000
0.1746977	0.6187110	0.1158468	0.4020923	0.2869106	1.0000000	0.7670987	0.1495259	0.2619755	0.0551907	0.0000000	0.3791561	0.3976295	0.6932020	0.5987302	0.9998912	0.0010027
1.0000000	0.8755249	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1314345	1.0000000	0.3036230	0.0020900	0.4177111	0.6586659	0.9695099	0.5987302	1.0000000	1.0000000
1.0000000	0.8399901	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1220371	1.0000000	0.4365701	0.9339010	0.2409707	0.6446129	0.9646604	0.5987302	0.7358133	1.0000000
1.0000000	0.6653628	0.4422312	0.4020923	0.5826017	0.9784777	0.9409064	0.1125850	0.0001793	0.2618582	0.0000887	0.7996825	0.4547887	0.8009078	0.5987302	0.0000223	0.9556542
1.0000000	0.8121585	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0993910	1.0000000	0.2468212	0.0000263	0.2167144	0.5834719	0.9352215	0.5987302	0.0000000	0.0000000
0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.7039228	0.0008456	0.3586216	0.0867211	0.9578770	0.8334665	0.9970076	0.5987302	1.0000000	1.0000000
0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.5498409	0.0041817	0.3429812	0.0320618	0.9578770	0.8095039	0.9955277	0.5987302	1.0000000	1.0000000
0.1746977	0.6187110	0.1034343	0.4020923	0.2692606	1.0000000	0.7466076	0.1495259	0.2619755	0.0551907	0.0000000	0.3240252	0.3976295	0.6932020	0.5987302	0.9985389	0.0010027
0.1746977	0.6187110	0.0590864	0.4020923	0.1948766	1.0000000	0.6337278	0.1495259	0.2619755	0.0551907	0.0000000	0.1309286	0.3976295	0.6932020	0.5987302	0.0027524	0.0010027
0.1746977	0.6187110	0.0539222	0.4020923	0.1845823	1.0000000	0.6138204	0.1495259	0.2619755	0.0551907	0.0000000	0.1113290	0.3976295	0.6932020	0.5987302	0.0003812	0.0010027
1.0000000	0.8903901	0.4422312	0.4020923	0.5826017	0.9999924	0.9409064	0.4654106	1.0000000	0.3940734	0.4907412	0.5835882	0.7949456	0.9943985	0.5987302	1.0000000	1.0000000
1.0000000	0.6791887	0.4422312	0.4020923	0.5826017	0.9429683	0.9409064	0.1113328	0.0008141	0.3312244	0.0145679	0.7265785	0.4578345	0.8056894	0.5987302	0.0000550	0.1306175
1.0000000	0.8799388	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1321769	1.0000000	0.3816957	0.3022853	0.4603419	0.6596567	0.9698296	0.5987302	0.0000001	1.0000000
1.0000000	0.8159064	0.4422312	0.4020923	0.5826017	0.0657280	0.9409064	0.1052080	1.0000000	0.3501545	0.0510865	0.2166557	0.4753332	0.8313988	0.5987302	0.0000000	0.0000000
0.1746977	0.6187110	0.0480083	0.4020923	0.1721909	1.0000000	0.5881360	0.1495259	0.2619755	0.0551907	0.0000000	0.0902845	0.3976295	0.6932020	0.5987302	0.0000313	0.0010027
0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.7039228	0.0000184	0.3971847	0.5405300	0.9578770	0.8334665	0.9970076	0.5987302	1.0000000	1.0000000
0.0077645	0.9231772	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0958644	0.0041538	0.3540419	0.0653443	0.9549522	0.5526204	0.9137070	0.5987302	0.0000000	1.0000000
0.9960341	0.9188009	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.5933598	0.9937809	0.3297358	0.0131588	0.9276758	0.8164466	0.9960019	0.5987302	1.0000000	1.0000000
1.0000000	0.8694436	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1433548	1.0000000	0.3079292	0.0028504	0.3681800	0.6730078	0.9738727	0.5987302	1.0000000	1.0000000
1.0000000	0.7449864	0.4422312	0.4020923	0.5826017	0.0008386	0.9409064	0.1010947	1.0000000	0.3782180	0.2567772	0.3585193	0.4911385	0.8521550	0.5987302	0.0000000	0.0000000
0.1746977	0.6187110	0.0464509	0.4020923	0.1688087	1.0000000	0.5807787	0.1495259	0.2619755	0.0551907	0.0000000	0.0850188	0.3976295	0.6932020	0.5987302	0.0000154	0.0010027
0.1746977	0.6187110	0.0601212	0.4020923	0.1968860	1.0000000	0.6374706	0.1495259	0.2619755	0.0551907	0.0000000	0.1349784	0.3976295	0.6932020	0.5987302	0.0040077	0.0010027
0.1746977	0.6187110	0.0660275	0.4020923	0.2080421	1.0000000	0.6574476	0.1495259	0.2619755	0.0551907	0.0000000	0.1587823	0.3976295	0.6932020	0.5987302	0.0300814	0.0010027

raster.values <- cbind.data.frame(RESULTS$ID, raster.values)
colnames(raster.values)<- c("ID", colnames(raster.values[,-1]))
kable(raster.values)

ID	MYYU	MYCA	MICI	MYVO	MYLU	LABL	MYEV	ANPA	MYTH	COTO	PAHE	EPFU	LANO	TABR	LACI	EUMA	EUPE
H2NFS8	0.1746977	0.6187110	0.0523755	0.4020923	0.1814072	1.0000000	0.6074240	0.1495259	0.2619755	0.0551907	0.0000000	0.1056724	0.3976295	0.6932020	0.5987302	0.0002035	0.0010027
H2NFS5	1.0000000	0.6476518	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.1417360	0.0000000	0.2467996	0.0000262	0.8744714	0.4065435	0.7122902	0.5987302	1.0000000	1.0000000
H5NFS2	0.1746977	0.6187110	0.0543530	0.4020923	0.1854588	1.0000000	0.6155645	0.1495259	0.2619755	0.0551907	0.0000000	0.1129229	0.3976295	0.6932020	0.5987302	0.0004526	0.0010027
H5NFS6	0.1746977	0.6187110	0.0836853	0.4020923	0.2386986	1.0000000	0.7060096	0.1495259	0.2619755	0.0551907	0.0000000	0.2350532	0.3976295	0.6932020	0.5987302	0.8526811	0.0010027
IB10	1.0000000	0.9005471	0.4422312	0.4020923	0.5826017	0.0009523	0.9409064	0.2914249	1.0000000	0.3854519	0.3559797	0.7230384	0.7541266	0.9900645	0.5987302	1.0000000	1.0000000
H2NFS9	0.3635506	0.6200561	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.1488006	0.5059076	0.0551907	0.0000000	0.9470735	0.3984167	0.6949216	0.5987302	1.0000000	0.0000199
H4FS8	0.9999994	0.9146396	0.4422312	0.4020923	0.5826017	0.9977640	0.9409064	0.4115917	0.9998384	0.3599207	0.0938242	0.8931081	0.7844351	0.9934588	0.5987302	1.0000000	1.0000000
H4NFS1	0.1746977	0.6187110	0.0518274	0.4020923	0.1802711	1.0000000	0.6051050	0.1495259	0.2619755	0.0551907	0.0000000	0.1036929	0.3976295	0.6932020	0.5987302	0.0001623	0.0010027
H4NFS5	0.1746977	0.6187110	0.0731587	0.4020923	0.2208694	1.0000000	0.6788340	0.1495259	0.2619755	0.0551907	0.0000000	0.1888414	0.3976295	0.6932020	0.5987302	0.2274919	0.0010027
H3FS8	1.0000000	0.8397064	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0967128	1.0000000	0.2754353	0.0002558	0.2403815	0.5645056	0.9226269	0.5987302	0.0000000	0.0000002
H4NFS10	0.1746977	0.6187110	0.0751250	0.4020923	0.2242944	1.0000000	0.6842758	0.1495259	0.2619755	0.0551907	0.0000000	0.1973338	0.3976295	0.6932020	0.5987302	0.3457951	0.0010027
H1NFS11	0.1746977	0.6187110	0.0658899	0.4020923	0.2077879	1.0000000	0.6570073	0.1495259	0.2619755	0.0551907	0.0000000	0.1582155	0.3976295	0.6932020	0.5987302	0.0287791	0.0010027
H4NFS9	0.1746977	0.6187110	0.0641996	0.4020923	0.2046441	1.0000000	0.6515040	0.1495259	0.2619755	0.0551907	0.0000000	0.1512973	0.3976295	0.6932020	0.5987302	0.0165205	0.0010027
H2NFS4	0.1746977	0.6187110	0.0444506	0.4020923	0.1643850	1.0000000	0.5709207	0.1495259	0.2619755	0.0551907	0.0000000	0.0784362	0.3976295	0.6932020	0.5987302	0.0000060	0.0010027
H1NFS12	0.1746977	0.6187110	0.0676257	0.4020923	0.2109749	1.0000000	0.6624818	0.1495259	0.2619755	0.0551907	0.0000000	0.1654060	0.3976295	0.6932020	0.5987302	0.0497388	0.0010027
H5FS4	1.0000000	0.8973325	0.4422312	0.4020923	0.5826017	0.1515941	0.9409064	0.3385960	1.0000000	0.2869110	0.0006107	0.6782625	0.7675747	0.9917059	0.5987302	1.0000000	1.0000000
IB15	1.0000000	0.9048351	0.4422312	0.4020923	0.5826017	0.9723134	0.9409064	0.3876448	0.9999995	0.4365624	0.9338712	0.7810998	0.7793024	0.9929586	0.5987302	1.0000000	1.0000000
H4FS11	1.0000000	0.8349757	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1012291	1.0000000	0.2753032	0.0002532	0.2317935	0.5922719	0.9404390	0.5987302	0.0337443	0.0000000
H3NFS1	0.1746977	0.6187110	0.0818469	0.4020923	0.2356713	1.0000000	0.7015910	0.1495259	0.2619755	0.0551907	0.0000000	0.2268686	0.3976295	0.6932020	0.5987302	0.7792485	0.0010027
H4NFS4	0.1746977	0.6187110	0.0836827	0.4020923	0.2386943	1.0000000	0.7060034	0.1495259	0.2619755	0.0551907	0.0000000	0.2350415	0.3976295	0.6932020	0.5987302	0.8525931	0.0010027
H1NFS9	0.1746977	0.6187110	0.0863798	0.4020923	0.2430748	1.0000000	0.7122625	0.1495259	0.2619755	0.0551907	0.0000000	0.2471107	0.3976295	0.6932020	0.5987302	0.9215102	0.0010027
OB4	1.0000000	0.7085289	0.4422312	0.4020923	0.5826017	0.9999994	0.9409064	0.1277455	1.0000000	0.0551907	0.0000000	0.5497607	0.4258041	0.7506282	0.5987302	0.9997273	0.0000000
H3FS7	1.0000000	0.8311463	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0997119	1.0000000	0.3768551	0.2401771	0.2264190	0.5851544	0.9362483	0.5987302	0.0000037	0.0004466
IB26	0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.7039228	0.0000493	0.3871204	0.3810857	0.9578770	0.8334665	0.9970076	0.5987302	1.0000000	1.0000000
H1FS3	1.0000000	0.7879798	0.4422312	0.4020923	0.5826017	0.3523369	0.9409064	0.1073739	1.0000000	0.2981489	0.0014033	0.2370232	0.4685517	0.8217859	0.5987302	0.0000000	0.0000000
H2FS6	1.0000000	0.7960210	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1095783	1.0000000	0.2753889	0.0002549	0.2265655	0.6192454	0.9542550	0.5987302	0.0000000	0.0000000
H5NFS11	0.1746977	0.6187110	0.1158468	0.4020923	0.2869106	1.0000000	0.7670987	0.1495259	0.2619755	0.0551907	0.0000000	0.3791561	0.3976295	0.6932020	0.5987302	0.9998912	0.0010027
H5FS3	1.0000000	0.8755249	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1314345	1.0000000	0.3036230	0.0020900	0.4177111	0.6586659	0.9695099	0.5987302	1.0000000	1.0000000
IB27	1.0000000	0.8399901	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1220371	1.0000000	0.4365701	0.9339010	0.2409707	0.6446129	0.9646604	0.5987302	0.7358133	1.0000000
H5FS7	1.0000000	0.6653628	0.4422312	0.4020923	0.5826017	0.9784777	0.9409064	0.1125850	0.0001793	0.2618582	0.0000887	0.7996825	0.4547887	0.8009078	0.5987302	0.0000223	0.9556542
H2FS12	1.0000000	0.8121585	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0993910	1.0000000	0.2468212	0.0000263	0.2167144	0.5834719	0.9352215	0.5987302	0.0000000	0.0000000
H5FS12	0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.7039228	0.0008456	0.3586216	0.0867211	0.9578770	0.8334665	0.9970076	0.5987302	1.0000000	1.0000000
H1FS8	0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.5498409	0.0041817	0.3429812	0.0320618	0.9578770	0.8095039	0.9955277	0.5987302	1.0000000	1.0000000
H5NFS10	0.1746977	0.6187110	0.1034343	0.4020923	0.2692606	1.0000000	0.7466076	0.1495259	0.2619755	0.0551907	0.0000000	0.3240252	0.3976295	0.6932020	0.5987302	0.9985389	0.0010027
H3NFS9	0.1746977	0.6187110	0.0590864	0.4020923	0.1948766	1.0000000	0.6337278	0.1495259	0.2619755	0.0551907	0.0000000	0.1309286	0.3976295	0.6932020	0.5987302	0.0027524	0.0010027
H3NFS4	0.1746977	0.6187110	0.0539222	0.4020923	0.1845823	1.0000000	0.6138204	0.1495259	0.2619755	0.0551907	0.0000000	0.1113290	0.3976295	0.6932020	0.5987302	0.0003812	0.0010027
H3FS1	1.0000000	0.8903901	0.4422312	0.4020923	0.5826017	0.9999924	0.9409064	0.4654106	1.0000000	0.3940734	0.4907412	0.5835882	0.7949456	0.9943985	0.5987302	1.0000000	1.0000000
H3FS12	1.0000000	0.6791887	0.4422312	0.4020923	0.5826017	0.9429683	0.9409064	0.1113328	0.0008141	0.3312244	0.0145679	0.7265785	0.4578345	0.8056894	0.5987302	0.0000550	0.1306175
H1FS5	1.0000000	0.8799388	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1321769	1.0000000	0.3816957	0.3022853	0.4603419	0.6596567	0.9698296	0.5987302	0.0000001	1.0000000
H1FS4	1.0000000	0.8159064	0.4422312	0.4020923	0.5826017	0.0657280	0.9409064	0.1052080	1.0000000	0.3501545	0.0510865	0.2166557	0.4753332	0.8313988	0.5987302	0.0000000	0.0000000
H3NFS3	0.1746977	0.6187110	0.0480083	0.4020923	0.1721909	1.0000000	0.5881360	0.1495259	0.2619755	0.0551907	0.0000000	0.0902845	0.3976295	0.6932020	0.5987302	0.0000313	0.0010027
H5FS10	0.0018331	0.9237512	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.7039228	0.0000184	0.3971847	0.5405300	0.9578770	0.8334665	0.9970076	0.5987302	1.0000000	1.0000000
H2FS5	0.0077645	0.9231772	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.0958644	0.0041538	0.3540419	0.0653443	0.9549522	0.5526204	0.9137070	0.5987302	0.0000000	1.0000000
H2FS2	0.9960341	0.9188009	0.4422312	0.4020923	0.5826017	1.0000000	0.9409064	0.5933598	0.9937809	0.3297358	0.0131588	0.9276758	0.8164466	0.9960019	0.5987302	1.0000000	1.0000000
H4FS9	1.0000000	0.8694436	0.4422312	0.4020923	0.5826017	0.0000000	0.9409064	0.1433548	1.0000000	0.3079292	0.0028504	0.3681800	0.6730078	0.9738727	0.5987302	1.0000000	1.0000000
H4FS1	1.0000000	0.7449864	0.4422312	0.4020923	0.5826017	0.0008386	0.9409064	0.1010947	1.0000000	0.3782180	0.2567772	0.3585193	0.4911385	0.8521550	0.5987302	0.0000000	0.0000000
H3NFS10	0.1746977	0.6187110	0.0464509	0.4020923	0.1688087	1.0000000	0.5807787	0.1495259	0.2619755	0.0551907	0.0000000	0.0850188	0.3976295	0.6932020	0.5987302	0.0000154	0.0010027
H2NFS6	0.1746977	0.6187110	0.0601212	0.4020923	0.1968860	1.0000000	0.6374706	0.1495259	0.2619755	0.0551907	0.0000000	0.1349784	0.3976295	0.6932020	0.5987302	0.0040077	0.0010027
H1NFS5	0.1746977	0.6187110	0.0660275	0.4020923	0.2080421	1.0000000	0.6574476	0.1495259	0.2619755	0.0551907	0.0000000	0.1587823	0.3976295	0.6932020	0.5987302	0.0300814	0.0010027

colnames(raster.values[,-1])

##  [1] "MYYU" "MYCA" "MICI" "MYVO" "MYLU" "LABL" "MYEV" "ANPA" "MYTH" "COTO"
## [11] "PAHE" "EPFU" "LANO" "TABR" "LACI" "EUMA" "EUPE"

NoFireValues <- filter(raster.values, grepl('NF|OB', ID))
length(NoFireValues$ID)

## [1] 24

FireValues <- filter(raster.values, !grepl('NF|OB', ID))
length(FireValues$ID)

## [1] 25

summary(FireValues)

##        ID          MYYU               MYCA             MICI       
##  H1FS3  : 1   Min.   :0.001833   Min.   :0.6654   Min.   :0.4422  
##  H1FS4  : 1   1st Qu.:0.999999   1st Qu.:0.8159   1st Qu.:0.4422  
##  H1FS5  : 1   Median :1.000000   Median :0.8755   Median :0.4422  
##  H1FS8  : 1   Mean   :0.800445   Mean   :0.8527   Mean   :0.4422  
##  H2FS12 : 1   3rd Qu.:1.000000   3rd Qu.:0.9146   3rd Qu.:0.4422  
##  H2FS2  : 1   Max.   :1.000000   Max.   :0.9238   Max.   :0.4422  
##  (Other):19                                                       
##       MYVO             MYLU             LABL              MYEV       
##  Min.   :0.4021   Min.   :0.5826   Min.   :0.00000   Min.   :0.9409  
##  1st Qu.:0.4021   1st Qu.:0.5826   1st Qu.:0.00000   1st Qu.:0.9409  
##  Median :0.4021   Median :0.5826   Median :0.06573   Median :0.9409  
##  Mean   :0.4021   Mean   :0.5826   Mean   :0.41852   Mean   :0.9409  
##  3rd Qu.:0.4021   3rd Qu.:0.5826   3rd Qu.:0.99776   3rd Qu.:0.9409  
##  Max.   :0.4021   Max.   :0.5826   Max.   :1.00000   Max.   :0.9409  
##                                                                      
##       ANPA              MYTH                COTO       
##  Min.   :0.09586   Min.   :0.0000184   Min.   :0.2468  
##  1st Qu.:0.10521   1st Qu.:0.0041817   1st Qu.:0.2981  
##  Median :0.13143   Median :1.0000000   Median :0.3502  
##  Mean   :0.27275   Mean   :0.7201544   Mean   :0.3413  
##  3rd Qu.:0.41159   3rd Qu.:1.0000000   3rd Qu.:0.3817  
##  Max.   :0.70392   Max.   :1.0000000   Max.   :0.4366  
##                                                        
##       PAHE                EPFU             LANO             TABR       
##  Min.   :0.0000263   Min.   :0.2167   Min.   :0.4548   Min.   :0.8009  
##  1st Qu.:0.0014033   1st Qu.:0.2404   1st Qu.:0.5645   1st Qu.:0.9226  
##  Median :0.0510865   Median :0.5836   Median :0.6587   Median :0.9695  
##  Mean   :0.1919979   Mean   :0.5736   Mean   :0.6595   Mean   :0.9415  
##  3rd Qu.:0.3022853   3rd Qu.:0.8931   3rd Qu.:0.7844   3rd Qu.:0.9935  
##  Max.   :0.9339010   Max.   :0.9579   Max.   :0.8335   Max.   :0.9970  
##                                                                        
##       LACI             EUMA             EUPE          
##  Min.   :0.5987   Min.   :0.0000   Min.   :0.0000000  
##  1st Qu.:0.5987   1st Qu.:0.0000   1st Qu.:0.0000002  
##  Median :0.5987   Median :0.7358   Median :1.0000000  
##  Mean   :0.5987   Mean   :0.5108   Mean   :0.6434687  
##  3rd Qu.:0.5987   3rd Qu.:1.0000   3rd Qu.:1.0000000  
##  Max.   :0.5987   Max.   :1.0000   Max.   :1.0000000  
## 

summary(NoFireValues)

##        ID          MYYU             MYCA             MICI        
##  H1NFS11: 1   Min.   :0.1747   Min.   :0.6187   Min.   :0.04445  
##  H1NFS12: 1   1st Qu.:0.1747   1st Qu.:0.6187   1st Qu.:0.05425  
##  H1NFS5 : 1   Median :0.1747   Median :0.6187   Median :0.06683  
##  H1NFS9 : 1   Mean   :0.2513   Mean   :0.6237   Mean   :0.11517  
##  H2NFS4 : 1   3rd Qu.:0.1747   3rd Qu.:0.6187   3rd Qu.:0.08436  
##  H2NFS5 : 1   Max.   :1.0000   Max.   :0.7085   Max.   :0.44223  
##  (Other):18                                                      
##       MYVO             MYLU             LABL        MYEV       
##  Min.   :0.4021   Min.   :0.1644   Min.   :1   Min.   :0.5709  
##  1st Qu.:0.4021   1st Qu.:0.1852   1st Qu.:1   1st Qu.:0.6151  
##  Median :0.4021   Median :0.2095   Median :1   Median :0.6600  
##  Mean   :0.4021   Mean   :0.2569   Mean   :1   Mean   :0.6919  
##  3rd Qu.:0.4021   3rd Qu.:0.2398   3rd Qu.:1   3rd Qu.:0.7076  
##  Max.   :0.4021   Max.   :0.5826   Max.   :1   Max.   :0.9409  
##                                                                
##       ANPA             MYTH            COTO              PAHE          
##  Min.   :0.1277   Min.   :0.000   Min.   :0.05519   Min.   :0.000e+00  
##  1st Qu.:0.1495   1st Qu.:0.262   1st Qu.:0.05519   1st Qu.:0.000e+00  
##  Median :0.1495   Median :0.262   Median :0.05519   Median :0.000e+00  
##  Mean   :0.1483   Mean   :0.292   Mean   :0.06317   Mean   :1.093e-06  
##  3rd Qu.:0.1495   3rd Qu.:0.262   3rd Qu.:0.05519   3rd Qu.:0.000e+00  
##  Max.   :0.1495   Max.   :1.000   Max.   :0.24680   Max.   :2.623e-05  
##                                                                        
##       EPFU              LANO             TABR             LACI       
##  Min.   :0.07844   Min.   :0.3976   Min.   :0.6932   Min.   :0.5987  
##  1st Qu.:0.11252   1st Qu.:0.3976   1st Qu.:0.6932   1st Qu.:0.5987  
##  Median :0.16209   Median :0.3976   Median :0.6932   Median :0.5987  
##  Mean   :0.24965   Mean   :0.3992   Mean   :0.6965   Mean   :0.5987  
##  3rd Qu.:0.23807   3rd Qu.:0.3976   3rd Qu.:0.6932   3rd Qu.:0.5987  
##  Max.   :0.94707   Max.   :0.4258   Max.   :0.7506   Max.   :0.5987  
##                                                                      
##       EUMA                EUPE         
##  Min.   :0.0000060   Min.   :0.000000  
##  1st Qu.:0.0004347   1st Qu.:0.001003  
##  Median :0.0399101   Median :0.001003  
##  Mean   :0.3796087   Mean   :0.042545  
##  3rd Qu.:0.8698884   3rd Qu.:0.001003  
##  Max.   :1.0000000   Max.   :1.000000  
## 

myyuF<- FireValues[,2]
myyuNF<-NoFireValues[,2]
myyuT <- t.test(myyuF,myyuNF)

mycaF <- FireValues[,3]
mycaNF <-NoFireValues[,3]
mycaT <- t.test(mycaF,mycaNF)

myciF<- FireValues[,4]
myciNF<-NoFireValues[,4]
myciT <- t.test(myciF,myciNF)


myluF<- FireValues[,6]
myluNF<-NoFireValues[,6]
myluT <- t.test(myluF,myluNF)

lablF<- FireValues[,7]
lablNF<-NoFireValues[,7]
lablT <- t.test(lablF,lablNF)

myevF<- FireValues[,8]
myevNF<-NoFireValues[,8]
myevT<- t.test(myevF,myevNF)

anpaF<- FireValues[,9]
anpaNF<-NoFireValues[,9]
anpaT <- t.test(anpaF,anpaNF)

mythF<- FireValues[,10]
mythNF<-NoFireValues[,10]
mythT <- t.test(mythF,mythNF)

cotoF<- FireValues[,11]
cotoNF<-NoFireValues[,11]
cotoT <- t.test(cotoF,cotoNF)

paheF<- FireValues[,12]
paheNF<-NoFireValues[,12]
paheT <- t.test(paheF,paheNF)

epfuF<- FireValues[,13]
epfuNF<-NoFireValues[,13]
epfuT <- t.test(epfuF,epfuNF)

lanoF<- FireValues[,14]
lanoNF<-NoFireValues[,14]
lanoT <- t.test(lanoF,lanoNF)

tabrF<- FireValues[,15]
tabrNF<-NoFireValues[,15]
tabraT <- t.test(tabrF,tabrNF)

eumaF<- FireValues[,17]
eumaNF<-NoFireValues[,17]
eumaT<- t.test(eumaF,eumaNF)

eupeF<- FireValues[,18]
eupeNF<-NoFireValues[,18]
eupeT <- t.test(eupeF,eupeNF)

eupeT$statistic

##        t 
## 5.721444

eupeT$parameter

##       df 
## 32.59786

eupeT$p.value     

## [1] 2.285563e-06

eupeT$conf.int

## [1] 0.3871383 0.8147095
## attr(,"conf.level")
## [1] 0.95

eupeT$conf.int[1]

## [1] 0.3871383

eupeT$estimate

##  mean of x  mean of y 
## 0.64346874 0.04254482

eupeT$estimate[1]- eupeT$estimate[2]

## mean of x 
## 0.6009239

testT <- data.frame(Species = c("MYYU", "MYCA", "MICI","MYLU", "LABL", "MYEV", "ANPA", "MYTH", "COTO" ,"PAHE" ,"EPFU", "LANO", "TABRA", "EUMA", "EUPE"), 
t = c(myyuT$statistic, mycaT$statistic, myciT$statistic, myluT$statistic, lablT$statistic, myevT$statistic, anpaT$statistic, mythT$statistic, cotoT$statistic, paheT$statistic, epfuT$statistic, lanoT$statistic,tabraT$statistic, eumaT$statistic, eumaT$statistic), 
pvalue = c(myyuT$p.value, mycaT$p.value, myciT$p.value, myluT$p.value, lablT$p.value, myevT$p.value, anpaT$p.value, mythT$p.value, cotoT$p.value, paheT$p.value, epfuT$p.value, lanoT$p.value, tabraT$p.value, eumaT$p.value, eupeT$p.value), 
Difference = c((myyuT$estimate[1]- myyuT$estimate[2]), (mycaT$estimate[1]- mycaT$estimate[2]), (myciT$estimate[1]- myciT$estimate[2]), (myluT$estimate[1]- myluT$estimate[2]), (lablT$estimate[1]- lablT$estimate[2]), (myevT$estimate[1]- myevT$estimate[2]), (anpaT$estimate[1]- anpaT$estimate[2]), (mythT$estimate[1]- mythT$estimate[2]), (cotoT$estimate[1]- cotoT$estimate[2]), (paheT$estimate[1]- paheT$estimate[2]), (epfuT$estimate[1]- epfuT$estimate[2]), (lanoT$estimate[1]- lanoT$estimate[2]), (tabraT$estimate[1]- tabraT$estimate[2]), (eumaT$estimate[1]- eumaT$estimate[2]),(eupeT$estimate[1]- eupeT$estimate[2])), 
row.names = NULL)

kable(testT)

Species	t	pvalue	Difference
MYYU	5.8201734	0.0000010	0.5491034
MYCA	14.9537508	0.0000000	0.2289669
MICI	12.5672956	0.0000000	0.3270565
MYLU	12.3255185	0.0000000	0.3257019
LABL	-6.0405066	0.0000031	-0.5814813
MYEV	11.2278931	0.0000000	0.2489568
ANPA	2.7763900	0.0104829	0.1244853
MYTH	4.3816713	0.0001280	0.4281797
COTO	20.7894533	0.0000000	0.2780988
PAHE	3.4416329	0.0021280	0.1919968
EPFU	4.2342991	0.0001125	0.3239766
LANO	9.7532730	0.0000000	0.2602961
TABRA	18.1764086	0.0000000	0.2450361
EUMA	0.9667779	0.3386269	0.1311768
EUPE	0.9667779	0.0000023	0.6009239

write.csv(testT, "testT.csv")
# 
# br<-cbind.data.frame(myyu,myci, mylu, labl, myev, anpa, coto, epfu, lano, tabr, eupe)
# par(las = 2)
# boxplot(br, outline=FALSE, cex= 0.8, ylab="Difference from unburned occupancy")
# abline(h=0)

# library(vioplot)
# vioplot(myyu,myci, mylu, labl, myev, anpa, coto, epfu, lano, tabr, eupe, col="grey")

The End

#valuetable <- getValues(AllLayers2)
#km1 <- kmeans(na.omit(valuetable), centers = 5, iter.max = 100, nstart = 10)
# create a blank raster with default values of 0
#rNA <- setValues(raster(AllLayers2), 0)
#for(i in 1:nlayers(AllLayers2)){
  #rNA[is.na(AllLayers2[[i]])] <- 1
#}
# convert rNA to an integer vector
#rNA <- getValues(rNA)
# convert valuetable to a data.frame
#valuetable <- as.data.frame(valuetable)
# if rNA is a 0, assign the cluster value at that position
#valuetable$class[rNA==0] <- km1$cluster
# if rNA is a 1, assign an NA at that position
#valuetable$class[rNA==1] <- NA
# create a blank raster
#classes1 <- raster(AllLayers2)
# assign values from the 'class' column of valuetable
#classes1 <- setValues(classes1, valuetable$class)
#plot(classes1, legend=TRUE, colNA="black")
#More info on how to do this clasification in *https://geoscripting-wur.github.io/AdvancedRasterAnalysis/*

# best2.My.Yu2
# 
# summary(best2.My.Yu2)
# 
# names(best2.My.Yu2)
# 
# getP(best2.My.Yu2)
# 
# best2.My.Yu2['state']
# 
# coef(best2.My.Yu2, type='state')
# 
# # Variance-covariance matrix
# vcov(best2.My.Yu2, type='state')
# 
# # Confidence intervals using profiled likelihood
# confint(best2.My.Yu2, type='state', method='profile')
# 
# # Expected values
# fitted(best2.My.Yu2)
# 
# logLik(best2.My.Yu2)
# 
# 
# 
# # Predicted abundance at specified covariate values
# 
# 
# # Assess goodness-of-fit
# parboot(best2.My.Yu2)
# plot(best2.My.Yu2)

MYYU

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       36    35.64       0.00
## 001        0        1     3.27       1.58
## 010        0        2     3.27       0.50
## 100        0        7     3.27       4.24
## 110        0        1     0.78       0.06
## 111        0        1     0.19       3.53
## 0NANA      1        1     1.00       0.00
## 
## Chi-square statistic = 11.4753 
## Number of bootstrap samples = 5000
## P-value = 0.0636
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.31  3.29  5.02  7.24 49.82 
## 
## Estimate of c-hat = 2

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       36  35.6446     0.0035
## 001        0        1   3.2732     1.5787
## 010        0        2   3.2732     0.4953
## 100        0        7   3.2732     4.2432
## 110        0        1   0.7828     0.0602
## 111        0        1   0.1872     3.5283
## 0NANA      1        1   0.9996     0.0000
## 
## Chi-square statistic = 11.4753 
## Number of bootstrap samples = 5000
## P-value = 0.0636
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.3101  3.2927  5.0179  7.2355 49.8200 
## 
## Estimate of c-hat = 1.9961

MYCA

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       14    13.90       0.00
## 001        0        1     2.31       0.74
## 010        0        4     2.03       1.90
## 011        0        3     4.88       0.72
## 100        0        5     2.11       3.97
## 101        0        2     4.38       1.29
## 110        0        3     4.60       0.56
## 111        0       16    13.80       0.35
## 1NANA      1        1     0.59       0.28
## 
## Chi-square statistic = 10.2136 
## Number of bootstrap samples = 5000
## P-value = 0.105
## 
## Quantiles of bootstrapped statistics:
##   0%  25%  50%  75% 100% 
##  0.5  3.9  5.6  7.8 22.0 
## 
## Estimate of c-hat = 1.66

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       14  13.8976     0.0008
## 001        0        1   2.3086     0.7418
## 010        0        4   2.0339     1.9006
## 011        0        3   4.8757     0.7216
## 100        0        5   2.1077     3.9689
## 101        0        2   4.3761     1.2902
## 110        0        3   4.5989     0.5559
## 111        0       16  13.8015     0.3502
## 1NANA      1        1   0.5939     0.2777
## 
## Chi-square statistic = 10.2136 
## Number of bootstrap samples = 5000
## P-value = 0.105
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.4966  3.9241  5.5556  7.8027 21.9891 
## 
## Estimate of c-hat = 1.6605

MYCI

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       38    38.09       0.00
## 010        0        2     1.47       0.19
## 100        0        3     1.49       1.53
## 101        0        1     1.17       0.02
## 110        0        2     1.03       0.91
## 111        0        2     1.75       0.04
## 0NANA      1        1     0.76       0.08
## 
## Chi-square statistic = 6.0021 
## Number of bootstrap samples = 5000
## P-value = 0.5146
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.29  4.22  6.11  8.63 58.13 
## 
## Estimate of c-hat = 0.87

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       38  38.0939     0.0002
## 010        0        2   1.4709     0.1903
## 100        0        3   1.4899     1.5304
## 101        0        1   1.1670     0.0239
## 110        0        2   1.0326     0.9063
## 111        0        2   1.7497     0.0358
## 0NANA      1        1   0.7580     0.0772
## 
## Chi-square statistic = 6.0021 
## Number of bootstrap samples = 5000
## P-value = 0.5146
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.2899  4.2157  6.1149  8.6268 58.1292 
## 
## Estimate of c-hat = 0.8695

MYVO

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       36    36.09       0.00
## 001        0        2     2.79       0.22
## 010        0        1     2.79       1.15
## 011        0        1     1.05       0.00
## 100        0        5     2.79       1.76
## 101        0        2     1.05       0.86
## 110        0        1     1.05       0.00
## 0NANA      1        1     0.89       0.01
## 
## Chi-square statistic = 4.5089 
## Number of bootstrap samples = 5000
## P-value = 0.5604
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.25  3.20  5.00  7.65 27.78 
## 
## Estimate of c-hat = 0.76

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       36  36.0932     0.0002
## 001        0        2   2.7867     0.2221
## 010        0        1   2.7867     1.1455
## 011        0        1   1.0503     0.0024
## 100        0        5   2.7867     1.7579
## 101        0        2   1.0503     0.8587
## 110        0        1   1.0503     0.0024
## 0NANA      1        1   0.8899     0.0136
## 
## Chi-square statistic = 4.5089 
## Number of bootstrap samples = 5000
## P-value = 0.5604
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.2471  3.2037  4.9999  7.6498 27.7826 
## 
## Estimate of c-hat = 0.759

MYLU

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       31    31.18       0.00
## 001        0        1     1.17       0.02
## 011        0        2     1.53       0.14
## 100        0        3     1.36       1.96
## 101        0        2     1.76       0.03
## 110        0        1     2.11       0.58
## 111        0        8     7.72       0.01
## 0NANA      1        1     0.82       0.04
## 
## Chi-square statistic = 4.1315 
## Number of bootstrap samples = 5000
## P-value = 0.702
## 
## Quantiles of bootstrapped statistics:
##     0%    25%    50%    75%   100% 
##   0.23   3.80   5.60   8.32 158.17 
## 
## Estimate of c-hat = 0.63

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       31  31.1849     0.0011
## 001        0        1   1.1707     0.0249
## 011        0        2   1.5309     0.1437
## 100        0        3   1.3637     1.9632
## 101        0        2   1.7609     0.0325
## 110        0        1   2.1059     0.5807
## 111        0        8   7.7246     0.0098
## 0NANA      1        1   0.8216     0.0387
## 
## Chi-square statistic = 4.1315 
## Number of bootstrap samples = 5000
## P-value = 0.702
## 
## Quantiles of bootstrapped statistics:
##       0%      25%      50%      75%     100% 
##   0.2317   3.8011   5.6000   8.3237 158.1747 
## 
## Estimate of c-hat = 0.6302

LABL

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       37    37.59       0.01
## 010        0        6     2.95       3.14
## 011        0        1     0.49       0.55
## 100        0        4     2.95       0.37
## 0NANA      1        1     0.91       0.01
## 
## Chi-square statistic = 8.1899 
## Number of bootstrap samples = 5000
## P-value = 0.215
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.53  3.26  4.98  7.62 87.10 
## 
## Estimate of c-hat = 1.36

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       37  37.5870     0.0092
## 010        0        6   2.9540     3.1409
## 011        0        1   0.4854     0.5457
## 100        0        4   2.9540     0.3704
## 0NANA      1        1   0.9058     0.0098
## 
## Chi-square statistic = 8.1899 
## Number of bootstrap samples = 5000
## P-value = 0.215
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.5313  3.2641  4.9751  7.6154 87.0951 
## 
## Estimate of c-hat = 1.3555

MYEV

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       12    12.21       0.00
## 001        0        4     4.01       0.00
## 010        0        4     3.96       0.00
## 011        0        3     5.39       1.06
## 100        0        7     3.95       2.36
## 101        0        5     5.16       0.00
## 110        0        2     5.23       2.00
## 111        0       11     8.09       1.05
## 0NANA      1        1     0.57       0.33
## 
## Chi-square statistic = 7.2442 
## Number of bootstrap samples = 5000
## P-value = 0.3074
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.77  3.94  5.68  7.84 22.15 
## 
## Estimate of c-hat = 1.16

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       12  12.2094     0.0036
## 001        0        4   4.0071     0.0000
## 010        0        4   3.9635     0.0003
## 011        0        3   5.3935     1.0622
## 100        0        7   3.9483     2.3587
## 101        0        5   5.1605     0.0050
## 110        0        2   5.2314     1.9960
## 111        0       11   8.0864     1.0498
## 0NANA      1        1   0.5654     0.3340
## 
## Chi-square statistic = 7.2442 
## Number of bootstrap samples = 5000
## P-value = 0.3074
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.7666  3.9414  5.6812  7.8422 22.1515 
## 
## Estimate of c-hat = 1.1624

ANPA

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       40    40.08       0.00
## 001        0        1     1.35       0.09
## 010        0        3     1.35       2.03
## 011        0        2     1.03       0.91
## 110        0        1     1.03       0.00
## 111        0        1     0.79       0.06
## 0NANA      1        1     0.69       0.13
## 
## Chi-square statistic = 5.9087 
## Number of bootstrap samples = 5000
## P-value = 0.4756
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
## 1.8e-04 4.0e+00 5.7e+00 7.7e+00 2.7e+01 
## 
## Estimate of c-hat = 0.96

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       40  40.0836     0.0002
## 001        0        1   1.3454     0.0887
## 010        0        3   1.3454     2.0349
## 011        0        2   1.0304     0.9125
## 110        0        1   1.0304     0.0009
## 111        0        1   0.7891     0.0564
## 0NANA      1        1   0.6947     0.1342
## 
## Chi-square statistic = 5.9087 
## Number of bootstrap samples = 5000
## P-value = 0.4756
## 
## Quantiles of bootstrapped statistics:
##        0%       25%       50%       75%      100% 
## 1.799e-04 3.997e+00 5.728e+00 7.680e+00 2.690e+01 
## 
## Estimate of c-hat = 0.964

MYTH

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       39    37.94       0.03
## 001        0        3     2.47       0.11
## 010        0        3     2.47       0.11
## 110        0        2     0.77       1.98
## 111        0        1     0.34       1.26
## 0NANA      1        1     1.00       0.00
## 
## Chi-square statistic = 7.5029 
## Number of bootstrap samples = 5000
## P-value = 0.22
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.19  3.41  5.03  7.16 26.80 
## 
## Estimate of c-hat = 1.33

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       39  37.9409     0.0296
## 001        0        3   2.4712     0.1131
## 010        0        3   2.4713     0.1131
## 110        0        2   0.7676     1.9786
## 111        0        1   0.3425     1.2620
## 0NANA      1        1   1.0000     0.0000
## 
## Chi-square statistic = 7.5029 
## Number of bootstrap samples = 5000
## P-value = 0.22
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.1902  3.4083  5.0258  7.1601 26.8014 
## 
## Estimate of c-hat = 1.3269

COTO

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       41    41.14       0.00
## 001        0        1     1.43       0.13
## 100        0        3     1.43       1.72
## 110        0        3     0.73       7.03
## 0NANA      1        1     0.87       0.02
## 
## Chi-square statistic = 12.3006 
## Number of bootstrap samples = 5000
## P-value = 0.0562
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.07  3.55  5.21  7.67 29.52 
## 
## Estimate of c-hat = 2.06

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       41  41.1374     0.0005
## 001        0        1   1.4310     0.1298
## 100        0        3   1.4310     1.7203
## 110        0        3   0.7318     7.0305
## 0NANA      1        1   0.8690     0.0197
## 
## Chi-square statistic = 12.3006 
## Number of bootstrap samples = 5000
## P-value = 0.0562
## 
## Quantiles of bootstrapped statistics:
##       0%      25%      50%      75%     100% 
##  0.06961  3.54629  5.20852  7.67265 29.52286 
## 
## Estimate of c-hat = 2.0593

PAHE

## Warning in sqrt(diag(v)): NaNs produced

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## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## Warning in sqrt(diag(v)): NaNs produced

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       46    45.99       0.00
## 100        0        1     0.24       2.47
## 111        0        1     1.23       0.04
## 0NANA      1        1     1.00       0.00
## 
## Chi-square statistic = 3.0585 
## Number of bootstrap samples = 5000
## P-value = 0.029
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
## 6.8e-11 6.0e-03 2.8e-01 7.4e-01 7.4e+00 
## 
## Estimate of c-hat = 5.2

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       46  45.9931     0.0000
## 100        0        1   0.2360     2.4738
## 111        0        1   1.2289     0.0426
## 0NANA      1        1   1.0000     0.0000
## 
## Chi-square statistic = 3.0585 
## Number of bootstrap samples = 5000
## P-value = 0.029
## 
## Quantiles of bootstrapped statistics:
##        0%       25%       50%       75%      100% 
## 6.767e-11 5.968e-03 2.846e-01 7.357e-01 7.407e+00 
## 
## Estimate of c-hat = 5.2027

EPFU

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       30    30.99       0.03
## 001        0        2     2.19       0.02
## 010        0        2     1.75       0.04
## 011        0        5     2.56       2.33
## 100        0        5     1.82       5.59
## 111        0        4     4.18       0.01
## 0NANA      1        1     0.79       0.06
## 
## Chi-square statistic = 12.7869 
## Number of bootstrap samples = 5000
## P-value = 0.0612
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.31  3.72  5.59  8.15 33.24 
## 
## Estimate of c-hat = 2.01

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       30  30.9880     0.0315
## 001        0        2   2.1940     0.0172
## 010        0        2   1.7508     0.0355
## 011        0        5   2.5585     2.3300
## 100        0        5   1.8151     5.5885
## 111        0        4   4.1835     0.0080
## 0NANA      1        1   0.7898     0.0559
## 
## Chi-square statistic = 12.7869 
## Number of bootstrap samples = 5000
## P-value = 0.0612
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.3091  3.7194  5.5923  8.1542 33.2421 
## 
## Estimate of c-hat = 2.0121

LANO

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       24    23.79       0.00
## 001        0        3     1.96       0.55
## 010        0        3     1.96       0.55
## 011        0        1     3.74       2.00
## 100        0        3     1.96       0.55
## 101        0        1     3.74       2.00
## 110        0        3     3.74       0.14
## 111        0       10     7.13       1.16
## 1NANA      1        1     0.55       0.38
## 
## Chi-square statistic = 7.8034 
## Number of bootstrap samples = 5000
## P-value = 0.2326
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.76  3.87  5.48  7.57 24.28 
## 
## Estimate of c-hat = 1.29

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       24  23.7910     0.0018
## 001        0        3   1.9579     0.5547
## 010        0        3   1.9579     0.5547
## 011        0        1   3.7358     2.0034
## 100        0        3   1.9579     0.5547
## 101        0        1   3.7358     2.0034
## 110        0        3   3.7358     0.1449
## 111        0       10   7.1280     1.1571
## 1NANA      1        1   0.5469     0.3755
## 
## Chi-square statistic = 7.8034 
## Number of bootstrap samples = 5000
## P-value = 0.2326
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.7568  3.8743  5.4810  7.5723 24.2785 
## 
## Estimate of c-hat = 1.2884

TABR

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       13    12.54       0.02
## 001        0        4     4.19       0.01
## 010        0        2     4.07       1.05
## 011        0        6     5.23       0.11
## 100        0        7     4.00       2.25
## 101        0        4     5.03       0.21
## 110        0        2     5.13       1.91
## 111        0       10     7.82       0.61
## 1NANA      1        1     0.62       0.24
## 
## Chi-square statistic = 6.7919 
## Number of bootstrap samples = 5000
## P-value = 0.3634
## 
## Quantiles of bootstrapped statistics:
##    0%   25%   50%   75%  100% 
##  0.47  3.98  5.72  7.96 41.16 
## 
## Estimate of c-hat = 1.08

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       13  12.5390     0.0169
## 001        0        4   4.1921     0.0088
## 010        0        2   4.0651     1.0491
## 011        0        6   5.2275     0.1141
## 100        0        7   3.9995     2.2509
## 101        0        4   5.0259     0.2094
## 110        0        2   5.1298     1.9095
## 111        0       10   7.8210     0.6071
## 1NANA      1        1   0.6150     0.2410
## 
## Chi-square statistic = 6.7919 
## Number of bootstrap samples = 5000
## P-value = 0.3634
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.4737  3.9785  5.7180  7.9630 41.1633 
## 
## Estimate of c-hat = 1.0793

LACI

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       22    21.55       0.01
## 001        0        5     3.03       1.28
## 010        0        1     3.03       1.36
## 011        0        3     4.01       0.26
## 100        0        6     3.03       2.91
## 101        0        1     4.01       2.26
## 110        0        2     4.01       1.01
## 111        0        8     5.31       1.36
## 1NANA      1        1     0.34       1.27
## 
## Chi-square statistic = 12.3741 
## Number of bootstrap samples = 5000
## P-value = 0.0446
## 
## Quantiles of bootstrapped statistics:
##   0%  25%  50%  75% 100% 
##  0.6  3.9  5.6  7.7 28.9 
## 
## Estimate of c-hat = 2.03

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       22  21.5510     0.0094
## 001        0        5   3.0317     1.2779
## 010        0        1   3.0317     1.3616
## 011        0        3   4.0135     0.2559
## 100        0        6   3.0317     2.9062
## 101        0        1   4.0135     2.2627
## 110        0        2   4.0135     1.0102
## 111        0        8   5.3133     1.3586
## 1NANA      1        1   0.3411     1.2729
## 
## Chi-square statistic = 12.3741 
## Number of bootstrap samples = 5000
## P-value = 0.0446
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
##  0.6024  3.8843  5.5561  7.6562 28.8635 
## 
## Estimate of c-hat = 2.0271

EUMA

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       44    44.55       0.01
## 010        0        4     0.93      10.11
## 0NANA      1        1     1.00       0.00
## 
## Chi-square statistic = 12.6375 
## Number of bootstrap samples = 5000
## P-value = 0.0298
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
## 4.1e-09 2.2e+00 3.8e+00 5.9e+00 2.0e+02 
## 
## Estimate of c-hat = 2.75

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       44  44.5498     0.0068
## 010        0        4   0.9313    10.1113
## 0NANA      1        1   0.9995     0.0000
## 
## Chi-square statistic = 12.6375 
## Number of bootstrap samples = 5000
## P-value = 0.0298
## 
## Quantiles of bootstrapped statistics:
##        0%       25%       50%       75%      100% 
## 4.094e-09 2.217e+00 3.836e+00 5.943e+00 1.957e+02 
## 
## Estimate of c-hat = 2.7488

EUPE

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       43    42.81       0.00
## 010        0        2     1.51       0.16
## 011        0        1     0.21       2.99
## 100        0        2     1.51       0.16
## 0NANA      1        1     0.88       0.02
## 
## Chi-square statistic = 5.4007 
## Number of bootstrap samples = 5000
## P-value = 0.4376
## 
## Quantiles of bootstrapped statistics:
##      0%     25%     50%     75%    100% 
## 1.4e-09 2.8e+00 4.8e+00 7.7e+00 1.0e+02 
## 
## Estimate of c-hat = 0.89

## 
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
## 
## Pearson chi-square table:
## 
##       Cohort Observed Expected Chi-square
## 000        0       43  42.8082     0.0009
## 010        0        2   1.5116     0.1578
## 011        0        1   0.2093     2.9866
## 100        0        2   1.5116     0.1578
## 0NANA      1        1   0.8784     0.0168
## 
## Chi-square statistic = 5.4007 
## Number of bootstrap samples = 5000
## P-value = 0.4376
## 
## Quantiles of bootstrapped statistics:
##        0%       25%       50%       75%      100% 
## 1.369e-09 2.768e+00 4.829e+00 7.682e+00 1.020e+02 
## 
## Estimate of c-hat = 0.8949