Ghosts of weather past? Impact of past and present weather-related factors on the seasonal questing activity of Ixodes ricinus nymphs in southwestern Finland

Abstract

Background: Hard ticks are responsible for spreading several zoonotic infections globally. Of the main vector species in Europe, Ixodes ricinus, nymphal ticks cause the largest number of disease cases. Therefore, understanding the seasonal questing behaviour of this life stage is particularly crucial for public health. We assessed seasonal variation in questing abundance of I. ricinus nymphs on a tick-infested island in southwestern Finland. Our primary goal was to examine which abiotic factors, such as meteorological conditions from the recent past, influence the seasonal questing activity of I. ricinus nymphs, and whether these influences manifest similarly across different times and habitat types.

Methods: Ticks were collected in 2012-2021 by cloth dragging from five different biotopes around the island. Three 50-m study transects were placed in each biotope, for a total of 15 transects. Air temperature and relative humidity were measured at the moment of sampling. Daily temperature and rainfall readings were obtained from weather stations.

Results: Across all biotopes, the overall density of I. ricinus nymphs was 10.6 ticks/100 m². In total, 7082 nymphs were collected from a total sampled area of 67,500 m². Increasing nymph densities were observed during the 10-year study period, but the increase was not linear. Instead, an incremental jump in densities was observed in 2016. One weather-related explanatory factor remained in each of the statistical models for modelling the seasonal questing activity of ticks, when the progress of the season was already taken into account by week numbers.

Conclusions: Increasing nymph densities were observed during a 10-year study period. While temperature measurements taken during the time of dragging did not appear to greatly influence the observed tick numbers, the recent past temperature variables were significant in all the natural biotopes. The results suggest that, in the clearly seasonal climate of southwestern Finland, the main factors shaping phenological patterns of I. ricinus nymphs during their main activity period are the progress of the season and a heat-related reduction in questing activity.

Keywords: Abiotic factors; Longitudinal study; Temporal tick dynamics; Ticks; Time-lagged effects.

Fig. 1

Study location. The location of the study area, Seili Island, in the Archipelago Sea, SW Finland. The black dots represent the locations of the weather stations outside the study area (A, Artukainen (Turku); N, Nagu). Map source: ESRI (available at: www.esri.com)

Fig. 2

Study transects on the Island of Seili. Three transects were assigned to each biotope type, denoted with letter and number combinations. However, the transect A2 in an alder thicket needed to be moved permanently to another location (“A4”) in spring 2019. A, alder thicket; C, coniferous forest; D, deciduous forest; M, meadow; P, pasture. The dashed area indicates the location of the research institute, with the weather station on its roof. Map source: ESRI (available at: www.esri.com)

Fig. 3

Weekly densities of I. ricinus nymphs (individuals per 100 m²) in all habitats on the Island of Seili, SW Finland. Data are shown by collection year (2012–2021), weekly rainfall and weekly average temperatures (weeks 19–43 each year). Note the different scales of y-axes, highlighting largely differing tick numbers among the biotopes

Fig. 4

Boxplot of questing nymphal tick density per 100 m² in all habitats. Shaded bars show the median and interquartile range of values for each index, while whiskers show minimum and maximum values; dots represent outliers and stars extreme outliers

Fig. 5

Sampling data (symbols) and polynomials predicting nymphal tick numbers (lines) in different biotopes (1–5). Owing to high variation even within biotopes, the prediction is made for each study transect separately (three transects per biotope), using the statistical models described in “Statistical analyses”. Note the different scales of y-axes, highlighting largely differing tick numbers among the biotopes

Fig. 6

Visualised results of generalised linear mixed models for the total number of nymphs for five different biotopes. The year and transect were common explanatory factors in all models. Other explanatory variables for each model are shown inside the grey ovals