Table 1 Highest ranked candidate models relating red fox abundance (crossings 10 km⁻¹ 24 h⁻¹) to arable land (%), vole density index (low or high), lynx abundance (crossings 10 km⁻¹ 24 h⁻¹), and wolf occurrence.

Wolf occurrence measurement	Model parameters	df	ΔAIC_c	w _i
1a. Wolf abundance
	Arable land + Lynx	6	0	0.66
	Arable land	5	2.5	0.19
	Arable land + Lynx + Vole	7	4.7	0.06
	Arable land + Lynx + Wolf abundance	7	5.5	0.04
	Arable land + Vole	6	7.2	0.02
	Lynx	5	21.4	<0.01
	Intercept only	4	26.4	<0.01
	Vole	5	31.4	<0.01
	Wolf abundance	5	32.7	<0.01
1b. Wolf category
	Arable land + Lynx	6	0	0.69
	Arable land	5	2.5	0.20
	Arable land + Lynx + Vole	7	4.7	0.07
	Arable land + Vole	6	7.2	0.02
	Arable land + Lynx + Wolf category	9	7.5	0.02
	Lynx	5	21.4	<0.01
	Intercept only	4	26.4	<0.01
	Vole	5	31.4	<0.01
	Wolf category	7	36.6	<0.01
2. Wolf pack size
	Vole	5	0.0	0.21
	Arable land + Vole	6	0.5	0.16
	Arable land	5	0.9	0.13
	Arable land + Wolf pack size	6	0.9	0.13
	Intercept only	4	1.1	0.12
	Arable land + Vole + Wolf pack size	7	2.0	0.08
	Vole + Lynx	6	4.1	0.03
	Wolf pack size	5	4.2	0.03
	Vole + Wolf pack size	6	4.7	0.02
	Lynx	5	5.0	0.02
	Arable land × Wolf pack size	7	5.1	0.02
	Arable land + Vole + Lynx	7	5.2	0.02
3. Time since territory establishment
	Time	6	0.0	0.16
	Arable land + Time	7	0.1	0.15
	Vole	5	0.4	0.13
	Time + Vole	7	0.7	0.11
	Arable land + Vole	6	0.9	0.10
	Arable land + Time + Vole	8	0.9	0.10
	Arable land	5	1.4	0.08
	Intercept only	4	1.5	0.07
	Time + Lynx	7	4.3	0.02
	Vole + Lynx	6	4.5	0.02
	Lynx	5	5.4	0.01

We used four separate measurements of wolf occurrence: 1a) wolf abundance (crossings 10 km⁻¹ 24 h⁻¹); 1b) a four-level wolf category (I: inside an observed wolf territory, II: inside an average wolf territory, III: inside a maximum wolf territory, or IV: outside a wolf territory); 2) wolf pack size (range 2–8 individuals), using only triangles inside wolf category I; and 3) time since territory establishment (year of territory establishment, 1–2 years after establishment, or ≥3 years after establishment), using only triangles inside wolf category I. Wildlife triangle identity and year of survey were used as random effects to account for repeated measurements and year effects. For each model, we show degrees of freedom (df), difference in AIC_c relative to the highest-ranked model (ΔAIC_c), and AIC-weight (w _i). For simplicity, only models with w _i > 0.01, univariate models, and intercept-only model are shown. Red fox, lynx, and wolf abundances were surveyed by snow-tracking in wildlife triangles in south-central Sweden from 2001–2003. Wolves were also monitored in a national monitoring program and voles were monitored using snap traps during long-term monitoring at Grimsö Wildlife Research Area.

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