Effect of landscape features on the relationship between Ixodes ricinus ticks and their small mammal hosts

Parasites & Vectors

Table 5 Questing I. ricinus nymph abundance as a function of the I. ricinus larval occurrence index, small mammal abundance and landscape features

	Effect estimates (± SE) on questing I. ricinus nymph abundance: GLMNBs
Landscapes	All landscapes	Agricultural	Forest
Sub-model (included variables)	All variables	All variables	Landscape excluded
N transects	2 × 36	2 × 18	2 × 18
Intercept	3.26 (±0.117)***	3.11 (±0.194)***	3.28 (±0.093)***
Sampling year
Recruitment variable
Larva occurrence index spring t-1	0.325 (±0.129)*	0.540 (±0.219)**	0.513 (±0.090)***
Host variables
Wood mouse abundance spring t-1	0.417 (±0.135)**
Wood mouse abundance autumn t-1
Bank vole abundance spring t-1	−0.351 (±0.126)**
Bank vole abundance autumn t-1			0.446 (±0.091)***
Environmental variables
EcoL	−0.500 (±0.125)***		-
Wood		0.355 (±0.224)	-
ENN-Wood			-

Generalized linear models of the abundance of questing I. ricinus nymph abundance in spring in the agricultural landscapes, the forest landscapes, and all the landscapes combined. The response variable was modelled using a negative binomial error distribution (GLMNBs). The explanatory variables include the larval occurrence index the previous year (in spring and autumn), the rodent abundance the previous year (in spring and autumn), the landscape variables (except in the forest landscapes models) and the sampling year. Each model contains all the significant explanatory variables (i.e. multiple regressions). The slope and standard error of the numeric variables from the model with the lowest AICc are given (see text). Significant codes are “°”: alpha = 0.1, “*”: alpha = 0.05, “**”: alpha = 0.01 and “***”: alpha = 0.001. Significant estimates (p < 0.05) are in bold

ISSN: 1756-3305