Ticks are important vectors of pathogens to humans [1, 2]. Most of the tick’s life cycle is spent in the environment, where ticks develop, moult, and quest actively for a host . Temperature has a central role in the regulation of the tick life cycle, including the development of the moulting stages (or oviposited eggs) and the periods in which ticks quest for a host in the vegetation. During the winter, low temperatures prevent rapid development, so development progresses slowly until temperatures increase in spring. At northern latitudes, temperature is the main driving factor of the length of the tick life cycle by regulating the duration of developmental processes. The requirements of temperature for development are species-specific and commonly prevent the spread of ticks farther north, where total cumulative degrees in a year are too low to allow complete development. Mortality depends on water losses, which are regulated by the relative humidity and the air saturation deficit. During questing, ticks lose water that they normally regain by descending at intervals to the litter zone where they can reabsorb water vapour from the atmosphere [4, 5]. When the ticks are hydrated, they ascend to the vegetation. The seasonal activity of ticks is characterised by several cycles of ascending and descending movements in the vegetation, regulated by temperature and water loss. Therefore, the energy reserves of the tick plus its abilities to retain water, together with air water content and temperature, are the factors regulating the questing and survival of ticks in the field.
Such tight dependence of ticks on climate traits makes them susceptible to meteorological changes, which in turn affects their periods of activity, development, and mortality and expansion into new zones or retreat from colonised areas [6, 7]. Some of these shifts in distribution have been reported from field studies [8, 9]. In other cases, associations between climate and prevalence rates of tick-transmitted pathogens have been proposed based on empirical grounds  or meta-analyses of published data . However, the effect of the projected climatic trends over the rather complex life cycle of ticks and the dynamics of tick-transmitted pathogens are still poorly understood and subject to debate. Although a change in climate might play an important role in certain geographic regions, for much of Europe, non-climatic factors, such as host population dynamics, are becoming increasingly important in the recorded spread of the tick Ixodes ricinus. Similar explanations have been hypothesized for the increase in prevalence rates of other tick-transmitted pathogens, in particular those carried by the tick Hyalomma marginatum.
Methods of species distribution modelling have been applied to arthropods of medical importance to understand the factors limiting their distribution [13–15]. These quantitative tools combine observations of species occurrence with environmental features  and are increasingly applied to produce coherent estimates of distribution patterns of mosquitoes , sandflies , and ticks [7, 17]. The covariates of climate and vegetation with which these arthropods are associated can be used to gain information about the effects of future climate scenarios or even recent trends . Because this information can be produced on a timely basis, with internally consistent data sources, it is a useful tool for resource managers, policy makers, and scientists interested in tracking recent changes across large administrative or environmental scales. These models are becoming increasingly popular in mapping the expected environmental variables that limit the physiological response of an arthropod vector .
Although some studies have emphasised the suitability of yearly averaged covariates involving temperature and rainfall, in the interpretation of the climate niche of the target arthropod [7, 14], others have used sets of variables at monthly intervals or the orthogonal transformation of a time series of covariates, via principal component analysis (PCA) or Fourier transformation [19, 20]. It has been explicitly indicated  that the set of covariates chosen to explain the abiotic habitat ought to have a clear biological meaning, describing adequately the biological and ecological constraints of the species in the spatial range to be modelled. Without this biological background, numerous variables can produce models with highly reliable matching distributions that are only statistically relevant. Although there is a tendency to consider that these potential distributions represent the probable geographical range, they must be regarded only as the characterization of the range of abiotic conditions (corresponding to non-living factors in the environment) under which the organism may survive . These so-called “suitability maps” or “potential distribution species models” are interpretations of a similarity measure of the abiotic conditions at each pixel of the map with the conditions at the known range of the species. These maps are actually a projection into the spatial range of the inferences made on such a niche of the organism. Without a model aimed at describing every process of the life cycle of the target organism, it is necessary to carefully select the minimum set of covariates that adequately describe, without inflation, the variables driving the observed distribution.
A common problem in modelling the abiotic niche of arthropod vectors is the lack of assessment of the statistical issues derived from spatial autocorrelation (SA) and collinearity (CO) of the covariates. SA is the spatial co-variation of properties between records used for calibration of models  violating standard statistical techniques that assume independence among observations. SA thus arises from multiple points of “presence” for the organism to be modelled, not randomly distributed over the space . Patterns of species distributions may be spatially autocorrelated because of population dynamics and historical factors, including closely clustered surveys that lead to the observed pattern of occurrence . SA is thus a spatially related problem that leads to an overestimation of the sample size, inflating the statistical significance of the measured spatial relationships and increasing the likelihood of false positives (type I errors, ).
The problem of SA in the determination of the tick abiotic niche can be stated as follows. Consider a region of several square kilometres (i.e., representing a small fraction of its complete spatial range) where tick-transmitted pathogens are a concern and where active surveys for ticks are commonly carried out. The tick will be collected in such a range where the spatial variability of the climate covariates is low because it is a relatively small territory. These collections represent, however, a significant fraction of the complete distribution range of the tick, as reported, and contribute to populate the dataset of presences with closely located records having very similar abiotic “preferences”. This method biases the perceived niche of the tick because the tick has not been randomly collected in the context of the complete dataset. An additional problem is expected to arise when covariates are gridded interpolations of climate from recording stations. Consider the same territory for which only a few climate-recording stations exist. Even the best performing methods will interpolate a few points as a surface of data where large areas have almost the same values for the covariate. These surfaces are later used to know the niche at which ticks have been collected, which will result in a significant number of records in the tick presence dataset having similar values for the climate covariates and biasing our capture of its climate niche.
CO is a statistical phenomenon in which two or more covariates in a multiple regression model are highly correlated and presents a problem related to the internal structure of the covariates used to explain the distribution of the records. In our application, the typical situation involves the use of time series of covariates that are strongly correlated (e.g., the temperature in one month is expected to be very similar to the values of the following month). CO is thus a spatio-temporal problem originated in the structure of the covariates and not in the records used for calibration. A special situation exists when covariates are grid interpolations of climate point records. In this case, the problems are magnified because the interpolation algorithms use a set of discrete, irregularly spaced sites (the meteorological stations), and the temporal series of covariates will exhibit a high CO compared with the regularity and the continuity of remotely sensed measurements.
To take full advantage of the available resources, researchers need to know the extent to which different variables selected to drive the models may affect the final outcome. This study is aimed at identifying the optimal set of abiotic variables describing the environmental niche of the two prominent ticks Ixodes ricinus and Hyalomma marginatum. It was not a goal to evaluate the accuracy of different algorithms in producing different results or to produce ready-to-use maps. The overall aim rather was to gain a general knowledge of the main variables driving the distribution of these ticks and to identify some procedural gaps in the selection of the covariates because they are commonly targeted to sketch predictive maps applied to the improvement of human health. We explicitly sought to demonstrate that (i) no single method exists to produce the best map for ticks, (ii) covariates producing the best performing model have high colinearity and spatial autocorrelation, therefore rendering conclusions unreliable, and (iii) that the transformation of time series of covariates produce satisfactory results and remove most of the internal problems of covariates.