Table 2 Model selection results evaluating the effect of understory vegetation (veg) and distance to forest patch (dist) on estimated habitat use (Ψ) and detection probabilities (p) for selected species within oil palm plantations in the eastern plains of Colombia (n = 33 sites). Only the top supported models (Δ AICc < 2) are shown.

From: Land management strategies can increase oil palm plantation use by some terrestrial mammals in Colombia

Species/Model

Δ AIC

AIC w

k

−2 log like

Intercept

SE

Beta1

SE

Beta2

SE

Giant anteater

Ψ(.), p(.)

0.00

1.00

2

298.29

      

Lesser anteater

Ψ(veg), p(.)

0.00

0.53

3

165.42

−0.73

0.76

2.03

1.14

  

Ψ(.), p(.)

1.62

0.23

2

169.47

      

Nine-banded armadillo

Ψ(veg), p(.)

0.00

0.42

3

36.02

−23.65

4.38

22.74

4.38

  

Ψ(.), p(.)

0.79

0.28

2

38.81

      

Ψ(veg + dist), p(.)

1.78

0.17

4

35.80

−26.10

4.77

28.33

4.57

−0.50

0.79

Fox

Ψ(.), p(veg)

0.00

1.00

3

274.77

      

Jaguarundi

Ψ(.), p(.)

0.00

0.50

2

118.25

      

Ψ(veg + dist), p(.)

0.71

0.35

4

113.93

−12.47

9.14

22.13

#

1.79

1.37

Ocelot

Ψ(dist), p(.)

0.00

0.43

3

104.74

46.00

#

−6.44

#

  

Ψ(.), p(.)

0.16

0.40

2

107.33

      

Ψ(veg), p(.)

1.87

0.17

3

106.61

0.61

1.57

25.57

#

  

Crab-eating Raccoon

Ψ(dist), p(.)

0.00

0.45

3

76.94

−11.34

5.21

1.65

0.83

  

Ψ(.), p(.)

1.23

0.24

2

80.17

      

Ψ(veg + dist), p(.)

1.59

0.20

4

76.53

−12.72

5.41

0.83

1.29

1.77

0.82

White tailed deer

Ψ(veg), p(.)

0.00

0.64

3

119.14

−1.88

1.13

3.28

1.73

  

Ψ(veg + dist), p(.)

1.79

0.26

4

118.33

6.56

17.90

3.51

2.81

−1.31

2.76

Capybara

Ψ(veg + dist), p(.)

0.00

0.45

4

39.58

−4.57

16.54

22.07

14.28

−3.20

1.68

Ψ(dist), p(.)

0.10

0.42

3

41.68

16.44

10.01

−3.10

1.74

  

Common opossum

Ψ(.), p(.)

0.00

0.43

2

162.84

      

Ψ(dist), p(.)

1.24

0.23

3

161.65

−4.58

3.46

0.63

0.53

  

Ψ(veg), p(.)

1.38

0.21

3

161.79

−1.05

0.68

0.81

0.81

  
  1. Notes: Δ AICc: difference in AIC values between each model with the lowest AIC model (best model); AICω: Akaike weight.; k: number of parameters in the model; SE: standard error. Understory vegetation is a binary covariate with 0 = clean or low understory vegetation (the intercept), and 1 = medium to high understory (beta), nearest distance to forest in log10, # = high standard errors, this does not affect the direction or effect of the untransformed beta estimate (Hines, 2006).