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Table 2 Results of two GLMMs testing the influence of different landscape attributes (slope, presence of canopy gaps, elevation and FTs density) on the probability occurrence of a route segment used at least twice and four times within a certain quadrat. Group ID (N = 5) was included as random effect in the model. We compared the full model to a corresponding null model (with only random and control variables) using likelihood-ratio tests. All p-values < 0.05 are shown in bold for clarity

From: Arboreal route navigation in a Neotropical mammal: energetic implications associated with tree monitoring and landscape attributes

Response variable

Probability of locating a route used at least twice

Probability of locating a route used at least four times

Full null model comparison

χ2 = 23.2, d.f. = 5, p <  0.001

χ2 = 18.8, d.f. = 5, p = 0.002

Predictor variable

Est.

s.e.

CIlower

CIupper

p-value

Est.

s.e.

CIlower

CIupper

p-value

(Intercept)

−0.156

0.574

−1.567

1.209

a

− 3.126

0.298

−6.359

−2.531

a

Slope

0.139

0.104

−0.128

0.481

0.182

0.390

0.139

0.006

0.752

0.058

Presence of canopy gaps

−0.803

0.115

−1.088

−0.482

0.001

−0.354

0.186

−0.452

0.023

0.128

Elevation

0.446

0.328

−0.323

1.298

0.198

0.944

0.550

−0.363

2.237

0.173

FTs density

0.896

0.213

0.372

1.390

0.006

0.967

0.246

0.401

1.533

0.010

Elevation * FTs density

−0.280

0.210

−0.783

0.340

0.273

−0.439

0.140

−0.809

−0.374

0.041

Overlapping area b

0.274

0.136

−0.014

0.689

0.054

−1.000

0.203

−3.398

−0.437

0.003

Location within the HR b

−0.538

0.442

−1.609

0.518

0.255

−0.724

0.499

−1.414

0.838

0.190

Autocorrelation term b

2.487

0.072

2.347

2.632

<  0.000

2.943

0.100

1.636

3.252

<  0.000

  1. aNot shown because of having no meaningful or very limited interpretation
  2. bRepresent control predictors included in the model