Perspectives on modelling the distribution of ticks for large areas: so far so good?

Abstract

BackgroundThis paper aims to illustrate the steps needed to produce reliable correlative modelling for arthropod vectors, when process-driven models are unavailable. We use ticks as examples because of the (re)emerging interest in the pathogens they transmit. We argue that many scientific publications on the topic focus on: (i) the use of explanatory variables that do not adequately describe tick habitats; (ii) the automatic removal of variables causing internal (statistical) problems in the models without considering their ecological significance; and (iii) spatial pattern matching rather than niche mapping, therefore losing information that could be used in projections.MethodsWe focus on extracting information derived from modelling the environmental niche of ticks, as opposed to pattern matching exercises, as a first step in the process of identifying the ecological determinants of tick distributions. We perform models on widely reported species of ticks in Western Palaearctic to derive a set of covariates, describing the climate niche, reconstructing a Fourier transformation of remotely-sensed information.ResultsWe demonstrate the importance of assembling ecological information that drives the distribution of ticks before undertaking any mapping exercise, from which this kind of information is lost. We also show how customised covariates are more relevant to tick ecology than the widely used set of “Bioclimatic Indicators” (“Biovars”) derived from interpolated datasets, and provide programming scripts to easily calculate them. We demonstrate that standard pre-tailored vegetation categories also fail to describe tick habitats and are best used to describe absence rather than presence of ticks, but could be used in conjunction with the climate based suitability models.ConclusionsWe stress the better performance of climatic covariates obtained from remotely sensed information as opposed to interpolated explanatory variables derived from ground measurements which are flawed with internal issues affecting modelling performance. Extracting ecological conclusions from modelling projections is necessary to gain information about the variables driving the distribution of arthropod vectors. Mapping exercises should be a secondary aim in the study of the distribution of health threatening arthropods.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-016-1474-9) contains supplementary material, which is available to authorized users.

Effect of Climate and Land Use on the Spatio-Temporal Variability of Tick-Borne Bacteria in Europe

“…Environmental predictors were selected based on published evidence of their importance to tick populations [ 22 , 44 , 45 , 46 ]. For each sampling site, we obtained climatic and environmental data from RS and interpolated climatic datasets.…”

“…The prediction of spatial and temporal distribution of I. ricinus based on ecological and climatic factors has progressed from studies capturing the short term phenology of the ticks based on ground climate data with simple models [ 16 ] to more complex ones based on remote sensing data using correlative [ 16 , 17 ] or modified matrix [ 18 ] approaches. Remote sensing (RS) imagery has also proven to be very useful in predicting changes in habitat and climatic conditions at different temporal and spatial scales, and it has been widely used to map the distribution of several disease vectors [ 19 ], including I. ricinus [ 20 , 21 , 22 ].…”

Host Distribution Does Not Limit the Range of the Tick Ixodes ricinus but Impacts the Circulation of Transmitted Pathogens

“…A number of reports have argued for the use of predictors that are ecologically relevant to the target species (Glass et al, 1995 ; Guerra et al, 2002 ). In this sense, Araújo and Guisan ( 2006 ) stated that the “use of automated solutions to predictor selection…should not be seen as a substitution for preselecting sound eco-physiological predictors based on deep knowledge of the bio-geographical and ecological theory.” We already expressed our concerns about the reliability of interpolated variables in the building of predictive models (Estrada-Peña et al, 2016 ) and satellite-derived information seems to be far more robust than interpolated measures of climate, which otherwise retain its value to explain the weather conditions in a given interval of time. We adhered to published protocols (Estrada-Peña and de la Fuente, 2016 ) to obtain a time series of MODIS-derived satellite data regarding land surface temperature (LSTD) and the Normalized Difference Vegetation Index (NDVI) which is a measure of the photosynthetic activity of the vegetation.…”

Looking for Ticks from Space: Using Remotely Sensed Spectral Diversity to Assess Amblyomma and Hyalomma Tick Abundance

“…Vegetation indices highlight the properties of vegetation through a spectral transformation of two or more spectral bands [47]. The NDVI, computed at single-pixel level, is a measure of photosynthetic activity that has been widely used in spatial epidemiology as a proxy for forest cover or relative humidity within the vegetation layer [9,12,48,49].…”