On the complexity of measuring forests microclimate and interpreting its relevance in habitat ecology: the example of Ixodes ricinus ticks

Abstract

BackgroundEcological field research on the influence of meteorological parameters on a forest inhabiting species is confronted with the complex relations between measured data and the real conditions the species is exposed to. This study highlights this complexity for the example of Ixodes ricinus. This species lives mainly in forest habitats near the ground, but field research on impacts of meteorological conditions on population dynamics is often based on data from nearby official weather stations or occasional in situ measurements. In addition, studies use very different data approaches to analyze comparable research questions. This study is an extensive examination of the methodology used to analyze the impact of meteorological parameters on Ixodes ricinus and proposes a methodological approach that tackles the underlying complexity.MethodsOur specifically developed measurement concept was implemented at 25 forest study sites across Baden-Württemberg, Germany. Meteorological weather stations recorded data in situ and continuously between summer 2012 and autumn 2015, including relative humidity measures in the litter layer and different heights above it (50 cm, 2 m). Hourly averages of relative humidity were calculated and compared with data from the nearest official weather station.ResultsData measured directly in the forest can differ dramatically from conditions recorded at official weather stations. In general, data indicate a remarkable relative humidity decrease from inside to outside the forest and from ground to atmosphere. Relative humidity measured in the litter layer were, on average, 24% higher than the official data and were much more balanced, especially in summer.ConclusionsThe results illustrate the need for, and benefit of, continuous in situ measurements to grasp the complex relative humidity conditions in forests. Data from official weather stations do not accurately represent actual humidity conditions in forest stands and the explanatory power of short period and fragmentary in situ measurements is extremely limited. However, it is still an open question to what kind of meteorological data are necessary to answer specific questions in tick research. The comparison of research findings was hindered by the variety of information provided, which is why we propose details for future reporting.

Impact of abiotic factors, habitat type and urban wildlife on the ecology of hard ticks (Acari: Ixodidae) in urban and peri-urban habitats

“…Larvae are the most sensitive to high temperatures, which are usually associated with an increased mortality rate in I. ricinus , due to dehydration [ 58 ]. Boehnke et al [ 59 ] concluded that information gathered by using official weather data (i.e. meteoblue) is not always a good proxy of the actual microclimatic conditions the ticks experience.…”

Modeling Potential Habitat for Amblyomma Tick Species in California

“…Environmental predictor variables were standardized to a resolution of 30 arc seconds, which is equivalent to approximately 1 km 2 at the latitude of California. However, there is some mis-match in spatial scales, as ticks are also affected by microclimatic variables that vary on a much finer spatial scale ranging from a few centimeters to meters, for example soil temperature and humidity, which may vary markedly within an area [86,87].…”

The importance of study duration and spatial scale in pathogen detection—evidence from a tick-infested island

“…As such, they emphasize the small scale at which local environmental factors may potentially affect the spatial distribution of pathogens. In a recent study, Boehnke et al 59 found that humidity can vary between open areas and forests as well as within different layers of a forest, directly affecting questing conditions for ticks. Indeed, the same is likely true for various other variables affecting tick questing and living conditions in different biotopes and structures [e.g., ground floor temperatures in shaded forests vs. open meadows (biotope type), different levels of canopy cover and wind shelter based on tree density (forest structure)] 60 .…”