BackgroundCorrelative modelling combines observations of species occurrence with environmental variables to capture the niche of organisms. It has been argued for the use of predictors that are ecologically relevant to the target species, instead of the automatic selection of variables. Without such biological background, the forced inclusion of numerous variables can produce models that are highly inflated and biologically irrelevant. The tendency in correlative modelling is to use environmental variables that are interpolated from climate stations, or monthly estimates of remotely sensed features.MethodsWe produced a global dataset of abiotic variables based on the transformation by harmonic regression (time series Fourier transform) of monthly data derived from the MODIS series of satellites at a nominal resolution of 0.1°. The dataset includes variables, such as day and night temperature or vegetation and water availability, which potentially could affect physiological processes and therefore are surrogates in tracking the abiotic niche. We tested the capacities of the dataset to describe the abiotic niche of parasitic organisms, applying it to discriminate five species of the globally distributed tick subgenus Boophilus and using more than 9,500 published records.ResultsWith an average reliability of 82%, the Fourier-transformed dataset outperformed the raw MODIS-derived monthly data for temperature and vegetation stress (62% of reliability) and other popular interpolated climate datasets, which had variable reliability (56%–65%). The transformed abiotic variables always had a collinearity of less than 3 (as measured by the variance inflation factor), in contrast with interpolated datasets, which had values as high as 300.ConclusionsThe new dataset of transformed covariates could address the tracking of abiotic niches without inflation of the models arising from internal issues with the descriptive variables, which appear when variance inflation is higher than 10. The coefficients of the harmonic regressions can also be used to reconstruct the complete original time series, being an adequate complement for ecological, epidemiological, or phylogenetic studies. We provide the dataset as a free download under the GNU general public license as well as the scripts necessary to integrate other time series of data into the calculations of the harmonic coefficients.

We applied a process-driven model to evaluate the impact of climate scenarios for the years 2020, 2050, and 2080 on the life cycle of Hyalomma marginatum ticks in the western Palearctic. The net growth rate of the tick populations increased in every scenario tested compared to the current climate baseline. These results support the expectations of increased tick survival and increased population turnover in future climate scenarios. We included a basic evaluation of host movement based on rules connected to altitude, slope, size of the near patches, and inter-patch distances in the real landscape over the target area. Data on landscape were obtained from medium-resolution MODIS satellite imagery, which allowed us to test the potential spread of the populations. Such a model of host dispersal linked to the process-driven life cycle model demonstrated that eastern (Turkey, Russia, and Balkans) populations of H. marginatum currently are well separated and have little mixing with western (Italy, Spain, and northern Africa) populations. The northern limit is marked by the cold areas in the Balkans, Alps, and Pyrenees. Under the warmer conditions predicted by the climate scenarios, the exchange of ticks throughout new areas, previously free of the vector, is expected to increase, mainly in the Balkans and southern Russia, over the limit of the mountain ranges. Therefore, the northern limit of the tick range would increase. Additional studies are necessary to understand the implications of host changes in range and abundance for H. marginatum and Crimean-Congo hemorrhagic fever virus.

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BackgroundModelling the environmental niche and spatial distribution of pathogen-transmitting arthropods involves various quality and methodological concerns related to using climate data to capture the environmental niche. This study tested the potential of MODIS remotely sensed and interpolated gridded covariates to estimate the climate niche of the medically important ticks Ixodes ricinus and Hyalomma marginatum. We also assessed model inflation resulting from spatial autocorrelation (SA) and collinearity (CO) of covariates used as time series of data (monthly values of variables), principal components analysis (PCA), and a discrete Fourier transformation. Performance of the models was measured using area under the curve (AUC), autocorrelation by Moran’s I, and collinearity by the variance inflation factor (VIF).ResultsThe covariate spatial resolution slightly affected the final AUC. Consistently, models for H. marginatum performed better than models for I. ricinus, likely because of a species-derived rather than covariate effect because the former occupies a more limited niche. Monthly series of interpolated climate always better captured the climate niche of the ticks, but the SA was around 2 times higher and the maximum VIF between covariates around 30 times higher in interpolated than in MODIS-derived covariates. Interpolated or remotely sensed monthly series of covariates always had higher SA and CO than their transformations by PCA or Fourier. Regarding the effects of background point selection on AUC, we found that selection based on a set of rules for the distance to the core distribution and the heterogeneity of the landscape influenced model outcomes. The best selection relied on a random selection of points as close as possible to the target organism area of distribution, but effects are variable according to the species modelled.ConclusionTesting for effects of SA and CO is necessary before incorporating these covariates into algorithms building a climate envelope. Results support a higher SA and CO in an interpolated climate dataset than in remotely sensed covariates. Satellite-derived information has fewer drawbacks compared to interpolated climate for modelling tick relationships with environmental niche. Removal of SA and CO by a harmonic regression seems most promising because it retains both biological and statistical meaning.

Foci of tick species occur at large spatial scales. They are intrinsically difficult to detect because the effect of geographical factors affecting conceptual influence of climate gradients. Here we use a large dataset of occurrences of ticks in the Afrotropical region to outline the main associations of those tick species with the climate space. Using a principal components reduction of monthly temperature and rainfall values over the Afrotropical region, we describe and compare the climate spaces of ticks in a gridded climate space. The dendrogram of distances among taxa according to occurrences in the climate niche is used to draw functional groups, or clusters of species with similar occurrences in the climate space, as different from morphologically derived (taxonomical) groups. We aim to further define the drivers of species richness and endemism at such a grid as well as niche similarities (climate space overlap) among species. Groups of species, as defined from morphological traits alone, are uncorrelated with functional clusters. Taxonomically related species occur separately in the climate gradients. Species belonging to the same functional group share more niche among them than with species in other functional groups. However, niche equivalency is also low for species within the same taxonomic cluster. Thus, taxa evolving from the same lineage tend to maximize the occupancy of the climate space and avoid overlaps with other species of the same taxonomic group. Richness values are drawn across the gradient of seasonal variation of temperature, higher values observed in a portion of the climate space with low thermal seasonality. Richness and endemism values are weakly correlated with mean values of temperature and rainfall. The most parsimonious explanation for the different taxonomic groups that exhibit common patterns of climate space subdivision is that they have a shared biogeographic history acting over a group of ancestrally co-distributed organisms.