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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 variableProbability of locating a route used at least twiceProbability of locating a route used at least four times
Full null model comparisonχ2 = 23.2, d.f. = 5, p <  0.001χ2 = 18.8, d.f. = 5, p = 0.002
Predictor variableEst.s.e.CIlowerCIupperp-valueEst.s.e.CIlowerCIupperp-value
(Intercept)−0.1560.574−1.5671.209a− 3.1260.298−6.359−2.531a
Slope0.1390.104−0.1280.4810.1820.3900.1390.0060.7520.058
Presence of canopy gaps−0.8030.115−1.088−0.4820.001−0.3540.186−0.4520.0230.128
Elevation0.4460.328−0.3231.2980.1980.9440.550−0.3632.2370.173
FTs density0.8960.2130.3721.3900.0060.9670.2460.4011.5330.010
Elevation * FTs density−0.2800.210−0.7830.3400.273−0.4390.140−0.809−0.3740.041
Overlapping area b0.2740.136−0.0140.6890.054−1.0000.203−3.398−0.4370.003
Location within the HR b−0.5380.442−1.6090.5180.255−0.7240.499−1.4140.8380.190
Autocorrelation term b2.4870.0722.3472.632<  0.0002.9430.1001.6363.252<  0.000
  1. aNot shown because of having no meaningful or very limited interpretation
  2. bRepresent control predictors included in the model