Predicting the distribution of Ixodes ricinus in Europe: Integrating microclimatic factors into ecological niche models

doi:10.21203/rs.3.rs-4911200/v1

Download PDF

Research Article

Predicting the distribution of Ixodes ricinus in Europe: Integrating microclimatic factors into ecological niche models

https://doi.org/10.21203/rs.3.rs-4911200/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Ixodes ricinus, commonly known as the castor bean tick and sheep tick, is a significant vector of various diseases such as tick-borne encephalitis and Lyme borreliosis. Due to climate change, the distribution and activity of I. ricinus are expected to increase, leading to a rise in diseases transmitted by this species. Most of the distribution models and ecological niche models utilize macroclimate datasets such as Worldclim or CHELSA in order to map the distribution of disease transmitting ticks. However, microclimatic factors are crucial for the activity and survival of small arthropods. In this study, an ecological niche modeling approach was followed in order to assess the climatic suitability of I. ricinus using both microclimatic and macroclimatic parameters. A Microclimate model was built combining parameters from Soiltemp and Wordclim databases while a Macroclimate model was built with the CHELSA dataset. Also future suitabilities were projected using the Macroclimate model under SSP370 and SSP585 scenarios. Both Macroclimate and Microclimate models showed similar distributions, confirming the current distribution of I. ricinus. The most important climatic factors were seasonality, annual temperature range, humidity and precipitation. Future projections suggest a significant expansion in northern and eastern Europe with notable declines in southern regions.

Ecological Modeling

Parasitology

Ixodes ricinus

ecological niche modeling

microclimate

climate change

ticks

Maxent.

Climatic suitability was modeled for Ixodes ricinus with microclimatic and macroclimatic parameters.
Main limiting factors were seasonality, temperature range and precipitation
Estimations for the future shows an expansion toward north and east in Europe
A significant decline in the southern distributions is also expected

As anthropogenic climate change becomes increasingly apparent through rising mean annual temperatures and the significant increase in both the frequency and magnitude of extreme weather events, the investigation of the relationships between vector populations and climate has garnered much more prominence (Kilpatrick and Randolph, 2012; Dantas-Torres, 2015; Semenza and Suk, 2018; Rocklöv and Dubrow, 2020; Gilbert, 2021). Consequently, studying how vectors interact with their climatic environment, focusing on mechanistic and correlative models, has gained additional importance for human health and welfare (Estrada-Peña, 2008; Li et al., 2012; Zhao et al., 2021).

Ixodes ricinus is one of the most important tick species in Europe due to its role as the vector of Lyme disease and Tick-borne encephalitis (Jaenson and Lindgren, 2011; Ostfeld and Brunner, 2015; Estrada-Peña et al., 2016). The abiotic factors that affect the abundance and distribution of I. ricinus have long been studied. Specifically, humidity and vegetation litter layers have been noted as the most important requirements for the survival and host-seeking activity of the species (Knülle and Rudolph, 1982; Gray et al., 2016; Van Gestel et al., 2022). Geographical variation in temperature is another significant factor and has been suggested to influence the timing of host-seeking activity and molting to the next life stage (Jaenson and Lindgren, 2011; Gilbert et al., 2014). As a result of global warming, changes in precipitation patterns and increasing temperatures could increase mortality rates of I. ricinus, particularly due to the potential increase in droughts and cold periods (Dautel et al., 2016). Conversely, the occurrence of milder winters could create new distribution areas with altitudinal shifts, such as mountainous regions and higher latitudes in Europe (Materna et al., 2008; Jaenson and Lindgren, 2011; Martello et al., 2014; Hvidsten et al., 2020).

Ecological niche modeling (ENM) is one of the fundamental correlative ways of investigating the distribution of disease transmitting vectors including ticks (Raghavan et al., 2020; Alkishe et al., 2021; Moo-Llanes et al., 2021). Ecological niche models combine locations of species with environmental variables to build a representative multi dimensional niche of the species (Warren and Seifert, 2011; Peterson and Soberón, 2012). In addition to predicting the present potential niche of the species ecological niche models are utilized to project the possible future distributions under global circulation model scenarios (GCMs) (Carvalho et al., 2017; Aguilar-Domínguez et al., 2021; Alkishe and Peterson, 2022). Although most distribution models use macroclimatic parameters gathered from standardized weather stations, the conditions in microclimate can differ greatly from macroclimate (Maclean et al., 2021; Marcin et al., 2021) and recently there have been significant advances in including microclimatic parameters in distribution modeling (Lembrechts et al., 2019; Stark and Fridley, 2022). Microclimate might be more decisive than macroclimate for small arthropods like ticks as they are much more exposed to conditions close to the ground and under litter, and the conditions in the microclimate could greatly affect the survival and activity of ticks (Randolph and Storey, 1999; Lauterbach et al., 2013; Boehnke et al., 2017; Volk et al., 2022).

Several modeling studies have suggested the possible change in the distributional areas of I. ricinus in the future, which would bring together the risk of the dissemination of related diseases. The common conclusion drawn from these studies is the range expansion to a large part of Europe, North Africa, and the Middle East (Porretta et al., 2013; Alkishe et al., 2017; Cunze et al., 2022). However, a significant portion of the studies has included specimens from North Africa, which were later identified as Ixodes inopinatus (Estrada-Peña et al., 2014). Therefore, reevaluating the previous occurrence records is extremely necessary. Additionally, occurrence records from countries such as Turkey, where I. ricinus was observed but generally was neglected in these analyses will provide important information about the current and future distribution of the species. Also most previous studies except Cunze et al. (2022) on I. ricinus used CMIP5 and earlier global circulation models (GCMs) to predict the possible future climatic suitabilities and updated projections that use latest GCMs will provide better predictions.

The primary goal of this study was to predict areas climatically favorable for I. ricinus under both current and future climate conditions using the Maxent approach by incorporating microclimatic and macroclimatic variables. Tick presence data were gathered from current literature and a Macroclimate suitability model was built using CHELSA bioclimatic dataset while a Microclimate suitability model was built combining Soiltemp and Worldclim datasets. SSP370 and SSP585 projections for 2011–2040 and 2041–2070 periods were used to carry out the future projections with the Macroclimate model. Results from this study will be informative for evaluating the risk of tick-borne diseases and predicting future threats posed by emerging diseases transmitted by I. ricinus.

Species distribution data

To infer the current and future distribution of I. ricinus, various resources have been utilized to gather occurrence data: VectorMap (www.vectormap.org), Global Biodiversity Information Facility (www.gbif.org) and scientific literature (Boehnke et al., 2015; Estrada-Pena and De La Fuente, 2016; Krawczyk et al., 2022). Occurrence points from Turkey have been primarily derived from Hekimoğlu (2022), which utilized both morphological and molecular methods for sample identification. Additionally, field sampling data from 2023 (n = 2, İstanbul) have been included in the final local data set (n = 19).

Raw records were cleaned and refined by excluding localities, (1) where tick collection has been conducted from birds as the presence of ticks on birds does not necessarily indicate ticks establish populations in those areas; (2) where samples from North Africa were collected, as these samples have been redetermined as I. inopinatus (Rollins et al., 2023); (3) While I. inopinatus could potentially distribute southern Spain and Portugal, the absence of clear evidence distinguishing them from I. ricinus or I. inopinatus led us to retain the geographical points of these countries in the analyses. (4) Sampling information from the Mediterranean and Aegean regions of Turkey was excluded due to a lack of confirmation with scientific literature. Following these steps, a preliminary set of 4669 geographic location points was obtained.

Environmental variables

Microclimatic predictors from the Soiltemp (available at https://zenodo.org/records/7134169) dataset were used to build the first model (Lembrechts et al., 2020) with 30 arcsec (∼1 km) resolution. Soiltemp includes bioclimatic temperature variables similar to Worldclim and CHELSA datasets. However unlike these datasets, which use data interpolated from standardized weather stations, the environmental data in Soiltemp is interpolated from both observational and experimental data gathered from dataloggers placed in microclimate (in soil) worldwide. Another advantage for Soiltemp is that the most intense data gathering was carried out in Europe, the main distributional area of I. ricinus. As Soiltemp data includes only the temperature related parameters (the first 11 bioclimatic variables) they were combined with the precipitation parameters (Bioclimatic vars 11–17) and additionally vapor pressure and surface radiation parameters from the Worldclim dataset (Fick and Hijmans, 2017) available at https://www.worldclim.org and the main dataset was built. Worldclim parameters were chosen in combinations because CHELSA and Soiltemp had some compatibility issues in Maxent models. For the Macroclimate model and projections, data were gathered from the CHELSA dataset available at https://CHELSAclimate.org, (Karger et al., 2022) with 30 arcsec, (∼1 km) resolution. CHELSA dataset was chosen because in addition to featuring measurements for a more recent period includes additional parameters that are biologically more relevant to Ixodes ticks such as growth degree days above 5°C (Gdd5) (Jaenson and Lindgren, 2011; Gray et al., 2021), net primary productivity (Npp), which is related to vegetation cover important for ticks, growth season temperature (Gst) and growth season precipitation (Gsp). Additionally, since Bioclim variables 8, 9, 18 and 19 are found to include some inconsistencies related to spatial artifacts first found in the Worldclim dataset and also present in the CHELSA dataset (Escobar, 2020; Aguilar-Domínguez et al., 2021; Alkishe and Peterson, 2022), these were not used in the datasets (Soiltemp, Worldclim and CHELSA). For the two datasets, variables were clipped to the area of interest between latitudes − 20⁰ and 65⁰ and longitudes 20⁰ and 65⁰ WGS84. Geographic computations were done by QGIS (QGIS Geographic Information System, 2022), GDAL library in Python (Open Source Geospatial Foundation, 2022), and R version 4.2.2 (R Core Team, 2022).

Future Projections

As future projections are not available for the Microclimate dataset, global circulation models (GCM) available for the CHELSA dataset were used to build the future projections with the model selected for the CHELSA dataset. The scenarios included were SSP370 and SSP585 projections of UKESM1-0-LL (Sellar et al., 2019), MRI-ESM2-0 (Oshima et al., 2020), MPI-ESM1-2 (Mauritsen et al., 2019), IPSL-CM6-LR (Boucher et al., 2020) and GFDL-ESM4 (Dunne et al., 2020). These datasets are also available at https://CHELSA-climate.org. SSP (Shared Socioeconomic Pathway) scenarios improve the RCP (Representative Concentration Pathways) by including important socioeconomic parameters including urbanization, population growth and advances (Hausfather, 2020).

Ecological Niche Models

Locations falling outside environmental rasters were removed before building models. To reduce the spatial sampling bias and to remove any unnoticed duplicates, the locations for I. ricinus were thinned to 10, 20, 30, 40 and 50 km and five different location sets were obtained by using the spThin package for R (Aiello-Lammens et al., 2015). Each resulting occurrence dataset was split into a training set (70%), a cross-validation set for evaluating the candidate models (25%), and an independent test set for evaluation of final models (5%), which is crucial for showing the real predictive power of a model. To better predict the distribution of species an accessible M space is needed (Soberon and Peterson, 2005; Barve et al., 2011). To build the M space, a minimum convex polygon of 100 km buffer area around the location points were created for models using the ellipsenm package for R, available at https://github.com/marlonecobos/ellipsenm (Cobos et al., 2019) and environmental rasters were cut to this buffer area. For microclimate and macroclimate three sets were created by setting 0.9, 0.8 and 0.7 inter-variable correlation thresholds using the usdm package for R (Naimi, 2017) and also by consulting the evaluations of the jackknife results of preliminary maxent models. Also an additional data set were prepared for the Macroclimate depending on the previous models and jackknife results amounting to a total of three variable sets for Microclimate and four sets for the Macroclimate models (Table 1). Although microclimate sets also include some macroclimatic variables (precipitation variables), “Microclimate” name was chosen to differentiate it from the other data set which uses only macroclimatic variables.

Table 1

The environmental predictors used in different sets in the Microclimate Model and Macroclimate Model with explanation of the predictors in the first column.
	Microclimate Model				Macroclimate Model
	Set 1	Set 2	Set 3		Set 1	Set 2	Set 3	Set 4
SBio3 ^a (Isothermality)	√	x	x	Bio3 ^c (Isothermality)	√	√	√	√
SBio4 ^a (temperature seasonality)	√	√	√	Bio4 ^c (temperature seasonality)	√	√	x	x
SBio6 ^a (minimum temperature of the coldest month)	√	√	√	Bio5 ^c (maximum temp. of the warmest month)	√	x	x	x
SBio7 ^a (annual range of air temperature)	√	x	x	Bio6 ^c (minimum temperature of the coldest month)	√	√	x	x
Bio13^b (precipitation amount of the wettest month)	√	√	√	Bio7 ^c (annual range of air temperature)	√	√	√	√
Bio14 ^b (precipitation amount of the driest month)	√	√	x	Bio13 ^c (precipitation amount of the wettest month)	√	√	√	√
Bio15 ^b (precipitation seasonality)	√	√	√	Bio14 ^c (precipitation amount of the driest month)	√	√	√	√
Srad ^b (Solar radiation)	√	x	x	Bio15 ^c (precipitation seasonality)	√	√	√	√
Vapr ^b (water vapor pressure)	√	√	√	Gdd5 ^c (growing degree days heat sum above 5°C)	√	√	x	x
				Gst ^c (mean temperature of the growing season)	√	√	√	√
				Gsp ^c (precipitation amount of the growing season)	√	√	√	√
				Npp ^c (net primary productivity)	√	√	√	x
Data Sources ^a:Soiltemp ^b:Worldclim ^c:CHELSA

Ecological niche models were built according to the maximum entropy algorithm (Phillips et al., 2006) and with the Kuenm R package (Cobos et al., 2019) which includes MaxEnt version 3.4.4 (Steven et al., 2021). The first calibration models were built with features linear, quadratic, product and hinge (L, Q, P, H) with the regularization parameters 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4 and 5. To evaluate the models a minimum threshold of 0.7 was set for the AUC and a maximum threshold of 0.05 for the omission rate and also a partial receiver operating characteristic test (pROC) was used (Peterson and Soberón, 2012; Aguilar-Domínguez et al., 2021). Further post-analysis model selections were carried by Akaike information criterion corrected for small sample sizes (AICc) and models with ΔAICc values below 2 were selected as final models (Warren and Seifert, 2011; Nuñez-Penichet et al., 2021). These final models were used to build the final projections with 10 bootstrap replicates and they were also tested with the independent testing locations that were set aside before model training and cross-validation.

Raster maps which show the suitability for distribution were binarized using the 10th percentile training presence logistic threshold that is taken from maxent results (Liu et al., 2005; Kramer-Schadt et al., 2013). For the future projections 4 consensus maps were calculated by taking the average of GCM scenarios for SSP370 and SSP585 for periods 2011–2040 and 2041–2070. In order to minimize possible deviations due to the novelty of conditions compared to the original distribution area, models were transferred to the future conditions without any projection in extreme novel conditions (no extrapolation in the GCM scenarios) (Cobos et al., 2019). To assess the uncertainty in the models, the range (max val-min val) of the suitability maps was used to create uncertainty maps for every prediction. Additionally, to see the places where conditions are more extreme compared to the calibration area of the models, a mobility-oriented parity metric (MOP) analysis was carried out and extrapolation risk of transfer regions was calculated with nearest 10% reference using the MOP package for R (Owens et al., 2013; Alkishe et al., 2020; Flores-López et al., 2022). Four consensus maps were prepared by taking the average of MOP maps created for each GCM. All maps were created with QGIS software.

Model Parameters

As models that used locations thinned to a greater distance than 20 km had AUC values below 0.7, models created with 20 km thinned locations were selected which had a total of n = 1001 locations (Fig. 1.). Out of these locations ~ 70% (n = 713) were used to train the models and ~ 25% (n = 238) were used for cross-validation, while ~ 5% (n = 50) were set aside for final evaluation tests. For the Microclimate model, out of all 630 models, 627 were statistically significant while only 24 out of these passed the 0.05 omission rate criteria and only one model from these fulfilled the AICc. This final model was a linear product (LP) model with a regularization multiplier of 0.5 which used the second set and had an omission rate of 0.046 and pROC value ~ 0 with a mean area under the curve (AUC) ratio of 1.16. The final evaluations done on the independent tests showed an omission rate of 0.06, which is marginally above 0.05, on the other hand this means that the Microclimate model correctly classified approximately 47 points out of 50 from the independent test set. The contribution of 6 environmental variables to the final model are shown in Table 2. The most important environmental variable was SBio4, the temperature seasonality near soil with a contribution of 40.6%, which was followed by vapor pressure which had a contribution of 36% and then Bio14 (precipitation amount of wettest month) and Bio15 (precipitation seasonality) both having contributions of 9.6%. SBio6, the near soil minimum temperature of the coldest month, had 3.3% contribution.

Table 2

Percent contribution of environmental predictors to Microclimate Model and Macroclimate Model.
Microclimate Model		Macroclimate Model
Predictor	Percent contribution	Predictor	Percent contribution
SBio4 ^a	40.6	Bio7 ^c	52
Vapr ^b	36	Gsp ^c	26.5
Bio14 ^b	9.6	Bio15 ^c	12.4
Bio15 ^b	9.6	Bio3 ^c	5.6
SBio6 ^a	3.3	Gst ^c	1.4
Bio13 ^b	0.9	Bio13 ^c	1.1
		Bio14 ^c	1
Data Sources ^a:Soiltemp ^b:Worldclim ^c:CHELSA

As for the Macroclimate, AUC curves also fell below 0.7 beyond 20 km thinning in models using CHELSA dataset so the 20 km thinned locations were also used in the models. Out of 840 candidate models, 757 were statistically significant while 49 from these passed the 0.05 omission rate criteria and only one fulfilled both omission rate and AICc conditions. The final Macroclimate model was a linear quadratic product hinge (LQPH) model combining all features with a regularization multiplier of 2 that used the fourth variable set (Table 2). The model had an omission rate of 0.048 and pROC ~ 0. with a mean area under the curve (AUC) ratio of 1.14. Final evals with the independent test showed that the Macroclimate model had better results predicting the independent test with a final evaluation omission rate of 0.041 which is even better than the cross validation omission rate of the same model. Although this is better than the final evaluations of the Microclimate model the values are actually very close as 0.041 omission rate amounts to predicting 48 out of 50 independent test locations correctly. For the Macroclimate model, the biggest contributors were Bio7 (annual range of air temperature) with a contribution of 52%, more than half of the total contribution. It was followed by Gsp (growth season precipitation) which had a contribution of 26.5%, while Bio3 (Isothermality) had a contribution of 5.6%.

Current and Future Predictions

For the current and future prediction maps, binary thresholds were 0.2781 and 0.2805 with very similar 10 percentile training presence logistic threshold values for both Microclimate and Macroclimate models. Predicted areas of suitability and uncertainty can be seen in Fig. 2. Both climate models predicted a wide distributional area that covers much of Europe except gaps in mountainous areas and in southern Europe regions, especially Spain and Greece. The climatically suitable regions mostly coincide with the current distribution of I. ricinus. On the other hand, in western Asia, suitable regions are located on the coast of Black Sea. The microclimate model shows a wider suitable region in Caucasus while the Macroclimate model predicted a smaller suitable region in the south of Caspian Sea. Both models also predicted a small suitable region in the coast of North Africa, with the Microclimate model showing a slightly larger area in North Africa. On the other hand, it needs to be taken into consideration that the level of uncertainty is much higher in southern Europe and North Africa.

The consensus of the different GCM future projections are shown in Fig. 3. The predictions of the SSP370 for 2011–2040 period show a significant expansion in eastern and northern Europe while there are some declines in southern Europe, Balkans and Black Sea region. Interestingly, despite being a more extreme scenario, SSP585 projection for 2011–2040 showed a smaller area of expansion, while the pattern of decline was similar to SSP370 scenario. It can be seen that the level of uncertainty increases from western Europe to the peripheral regions where most of the expansion and decline occurs. Future projections for 2041–2070 suggest continuation of the increasing suitability in northeast Europe with the SSP370 scenario showing a spread that is slightly larger than SSP585. Conversely, the decline in the southern regions are larger in the SSP585 scenarios for 2041–2070.

Uncertainty and MOP results

For the present conditions of the Microclimate model uncertainty is relatively low within the main distributional area of I. ricinus, while highest levels of uncertainty is observed at the margins (Fig. 2), particularly east Mediterranean, south of Caucasus (Georgia), North African Coast, southwest of Spain and west of Scandinavia. Uncertainty attains highest values in east Mediterranean and North Africa, outside the distribution of I. ricinus. So the results of high suitability in these areas are not very reliable. Regarding the Macroclimate model, although the maximum level of uncertainty is higher compared to the Microclimate model, the overall level of uncertainty is lower as seen from Fig. 2. The general pattern of uncertainty is similar which increases towards the margins. Specifically east of Mediterranean, North Africa, west of Spain and western Scandinavia exhibit higher levels of uncertainty. Also uncertainty in future projections is greater than that in present projections. However, the pattern of uncertainty remains similar to the present, with western Europe (including France, the United Kingdom, and parts of Germany) experiencing the lowest uncertainty levels, while higher uncertainty is evident around the Mediterranean basin, the Balkans, western Spain, and western Scandinavia (see Fig. 3).

Mobility Oriented Parity (MOP) results can be seen in Fig. 4. In all future projections, areas with significant extreme extrapolation begin at south of Anatolia to Iran and Middle East, North Africa and the east of Caspian Sea. Almost all regions with significant extrapolation fall outside the current and predicted future distribution range of I. ricinus, except for small areas in southern Anatolia and North Africa. However, these particular areas also exhibit the highest levels of uncertainty.

Ixodes ricinus demonstrates a high adaptability to European environments and has a tendency to expand its geographic range towards northern altitudes, which is mostly explained by climate change (Materna et al., 2008; Jaenson and Lindgren, 2011; Martello et al., 2014; Hvidsten et al., 2020; Garcia-Vozmediano et al., 2020). This brings to the attention whether this expansion is likely to give rise to new, distant foci for Lyme and TBE diseases. Environmental parameters play a significant role in influencing the distribution of I. ricinus. Factors such as climatic conditions, habitat characteristics, and landscape heterogeneity have been identified as key determinants affecting the abundance and prevalence of this tick species (Krasnov et al., 2007; Ruiz-Fons et al., 2012; Hauser et al., 2018).

The Microclimate model identified temperature seasonality as the primary environmental predictor, while the Macroclimate model indicated the annual temperature range as the most significant contributor. These findings are consistent with previous studies. Annual temperature range, annual mean temperature, and annual precipitation were identified as the most influential factors by Alkishe et al., (2017). Similarly, temperature seasonality is considered a relevant factor according to Cunze et al., (2022). The second influential factors are vapor pressure and growth season precipitation according to Microclimate and Macroclimate models respectively. The off-host survival of ticks depend strongly on water availability since desiccation is one of the most prominent causes for tick mortality and decreasing questing activity of ticks (Perret et al., 2004; Tagliapietra et al., 2011; Nolzen et al., 2022; Van Gestel et al., 2022). Factors such as higher soil moisture content and increased cloud cover have been linked to increased questing activity of I. ricinus (Medlock et al., 2008; Lauterbach et al., 2013). Additionally, the distribution of I. ricinus has expanded in the past three decades due to more favorable biotic and abiotic conditions, which can be influenced by changes in precipitation and an increase in mean winter temperatures (Gray and Ogden, 2021). Other modeling studies have underscored the importance of precipitation as a significant factor in the distribution of this species in both current and future projections (Alkishe et al., 2017; Cunze et al., 2022). Temperature has been recognized in various studies as a crucial determinant in the completion of the life cycle and duration of host-seeking activity of I. ricinus in northern latitudes (Lindgren et al., 2000; Schwarz et al., 2012; Hauser et al., 2018). However Gst (growth season temperature) had a very small contribution in the final Macroclimate models and Gdd5 (degree days above 5 ⁰C) was not included in the final selected models. Another discrepancy with these previous studies is that minimum temperature of the coldest month (SBio6) contributed less compared to other variables in the Microclimate model and in the final selected Models for Macroclimate, Bio6 was not included due to correlation thresholds. However, when we included Bio6 in the side-models out of curiosity, we saw that Bio6 was behind other parameters with a contribution smaller than 3%. This might be due to the difference in the CHELSA and Soiltemp datasets we used in creating the models instead of Worldclim. Additionally, the relation between minimum temperatures and tick survival might be more complicated due to the buffering effect of microclimatic factors such as snow cover and leaf litter on tick survival and activity (Van Gestel et al., 2022). For example, in situ measurements have shown that humidity and temperature values in the understorey of forests where I. ricinus resides are indeed much more tepid and stable compared to that gathered from standard weather stations (Boehnke et al., 2015). Also, the increased winter mortality effect of removing leaf litter and snow cover on Ixodes scapularis was documented by experimental field plots (Volk et al., 2022).

The models indicate that the region climatically suitable for I. ricinus covers a great portion of Europe except southern areas in Spain and Greece and the coastal regions of the Black Sea in West Asia. This outcome is consistent with the results of previous models on I. ricinus. On the other hand, model predictions showed a more constrained distribution in the Southern regions of Europe especially in southern and western Anatolia, this is an expected result as we know that presently there is no record of I. ricinus in this region (Hekimoğlu, 2022). The model also revealed a small area of suitability on the North African coast, however the suitability of this region is not reliable due to the high uncertainty. Although some of the previous studies included I. ricinus from North Africa, there is high probability that some of these records are I. inopinatus as recent genomic analysis clearly demonstrated the presence of I. inopinatus in North Africa (Younsi et al., 2020; Rollins et al., 2023). Nevertheless, the absence of current and future distribution of I. ricinus in the Mediterranean region can not solely be ascribed to I. inopinatus. Although the distribution of I. inopinatus in different European countries, such as Germany, Spain, and Portugal, has been suggested (Estrada-Peña et al., 2014; Chitimia-Dobler et al., 2018; Hauck et al., 2019), genomic data has shown that German samples are in fact I. ricinus (Rollins et al., 2023). In this case, the taxonomic status of Mediterranean populations should be reevaluated, which could influence the projections of I. ricinus in these areas. Considering the taxonomic reevaluation mentioned above, many recent studies excluded North African populations from their dataset (Cunze et al., 2022; Noll et al., 2023). The findings of these studies largely align with our current and future predictions. A large part of Europe constitutes the distribution area of the species, with the most suitable areas located in Western Europe. While Mediterranean countries such as France and Italy include suitable areas for the species' distribution, Portugal and the northern part of Spain also appears suitable in the west Mediterranean. Along the Black Sea coast, the most suitable areas include coastal regions encompassing the northern part of Turkey.

Future projections indicate a spread in northern and eastern Europe, confirming other projections with different bioclimatic datasets (Cunze et al., 2022; Noll et al., 2023). A consensus is also observed with areas that will not be suitable in the future, particularly the predicted habitat loss in Spain, Greece, and the Balkans (Cunze et al., 2022). Another outcome is that in the near future scenarios (2011–2040) the midway scenario (SSP370) estimates a slightly wider new distributional area for I. ricinus in the northeast Europe compared to SSP585, a worse case scenario. This outcome is compatible with the previous future projections by Porretta et al., (2013) and also Alkishe et al., (2017), where midway scenarios also predicted a wider new region of suitability in near future. While the spread in the 2070 scenarios is similar between SSP370 and SSP585 scenarios, the decline in the southern distributional areas including Balkans and Mediterranean is much more prominent in SSP585, which is most probably due to the predictions of great declines in precipitation in this region. Several climatic studies in past decades since the early 2000’s deemed the Mediterranean basin one of the climate change hot spots, with projections showing increased temperature and aridity, heightened vulnerability to drought and high temperatures and a higher frequency of heat waves (Ulbrich et al., 2006; Giorgi and Lionello, 2008; Naumann et al., 2018). Furthermore the impacts of recent climate change on increased drought frequency and magnitude is already documented in Mediterranean type climates (Hoerling et al., 2012; Feng et al., 2019). Current projections indicate the most suitable distribution areas for the species in Turkey are the Black Sea coasts and the Thrace region. However, while smaller suitable areas are also indicated along the Mediterranean coast of the country, the uncertainty in these predicted areas is again quite high. Future projections suggest a loss of suitability in the southern coast and, additionally, declines in certain localities, especially in the inland areas of Thrace in the north. This pattern indicates that expected climate changes in the Mediterranean Basin in the future will result in a decrease in the distribution areas of the species in Turkey, similar to the projections for Spain.

Ecological Niche Models are valuable tools for assessing the possible present and future distribution of parasite vectors, However there are several caveats and special care is needed when building distribution models. Major challenges are the potential bias and limitations in the outputs of niche models, especially when projecting to future scenarios (Peterson et al., 2018) so it is crucial to provide necessary uncertainty values associated with these models. Additionally, ectoparasites including ticks partially depend on their hosts for survival and transportation pose other risks in modeling. Host species and its abundance is one of the most important factors in the distribution of ticks to new areas and establishing populations. Moreover, the vast numbers of migrating birds increase the probabilities for the geographic spread of I. ricinus and related diseases, emphasizing the importance of considering avian hosts in disease epidemiology (Waldenström et al., 2007; Ciebiera et al., 2019). Although hosts play a crucial role due to the transmission dynamics of ticks, there are some limitations associated with including hosts in modeling studies. For instance, I. ricinus is a three-host species feeding on different hosts during its life cycle (Hofmeester et al., 2016), which can introduce complexity when using multiple hosts in modeling studies. Recent papers have shown contrasting hypotheses regarding tick-host associations. Some suggest that ticks select hosts based on the environment, while others propose that ticks select environments and feed on any available host within those environments (Nava and Guglielmone, 2013; Zhang et al., 2019; Ginsberg et al., 2022; Estrada-Peña et al., 2023). More to the point, while it has been suggested that hosts can influence tick distribution at a smaller geographical scale, the distribution of ticks over a wide spatial scale is primarily determined by direct climatic effects rather than host presence (Cumming, 2002). Considering all these factors, the importance of understanding the interactions between hosts, environment, and ticks is essential for developing effective strategies to manage tick-borne diseases and control their spread.

In conclusion, this study aimed to update previous projections by reviewing coordinates and utilizing new datasets and additionally implementing microclimatic parameters to build assistive models that would complement other distribution models that are reliant on macroclimatic parameters. For species distribution models, we still depend on macroclimatic datasets since predictions of very fine grained parameters like microclimate for future are not yet available and macroclimate data would still provide good predictions in large scales. Therefore, it is crucial to continuously replicate, validate and update previous models for better predictions of the possible future distributions of disease transmitting ticks.

Data Availability.

Coordinates used in this study are available at https://data.mendeley.com/datasets/--- (dataset is under moderation and it will become public as soon as it has been approved)

Acknowledgements.

The numerical calculations reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).

Author’s contributions.

O. H. and A. C. K. designed the study; O. H. prepared and cleaned the data; A. C. K. carried out the analysis and interpreted the results; A. C. K. prepared the tables and maps; A. C. K. and O.H. prepared the original draft, reviewed and edited the manuscript.

Financial support.

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

Competing interest.

None.

Ethical Standards

Not applicable

Aguilar-Domínguez M, Moo-Llanes DA, Sánchez-Montes S, Becker I, Feria-Arroyo TP, de León AP and Romero-Salas D (2021) Potential distribution of Amblyomma mixtum (Koch, 1844) in climate change scenarios in the Americas. Ticks and Tick-borne Diseases 12, 101812.
Aiello‐Lammens ME, Boria RA, Radosavljevic A, Vilela B. and Anderson RP (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545.
Alkishe A and Peterson AT (2022). Climate change influences on the geographic distributional potential of the spotted fever vectors Amblyomma maculatum and Dermacentor andersoni. PeerJ 10, e13279.
Alkishe A, Peterson AT and Samy AM (2017) Climate change influences on the potential geographic distribution of the disease vector tick Ixodes ricinus. PLOS ONE 12, e0189092.
Alkishe A, Cobos ME, Peterson AT and Samy AM (2020) Recognizing sources of uncertainty in disease vector ecological niche models: An example with the tick Rhipicephalus sanguineus sensu lato. Perspectives in Ecology and Conservation 18, 91–102.
Alkishe A, Raghavan RK and Peterson AT (2021) Likely geographic distributional shifts among medically important tick species and tick-associated diseases under climate change in north america: A review. Insects 12, 1–28.
Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J and Villalobos F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222, 1810–1819.
Boehnke D, Brugger K, Pfäffle M, Sebastian P, Norra S, Petney T, Oehme R, Littwin N, Lebl K, Raith J, Walter M, Gebhardt R and Rubel F (2015) Estimating Ixodes ricinus densities on the landscape scale. International Journal of Health Geographics 14, 23.
Boehnke D, Gebhardt R, Petney T and Norra S (2017). On the complexity of measuring forests microclimate and interpreting its relevance in habitat ecology: The example of Ixodes ricinus ticks. Parasites and Vectors 10, 1–14. doi: 10.1186/s13071-017-2498-5.
Boucher O, Servonnat J, Albright AL, Aumont O, Balkanski Y, Bastrikov V, Bekki S, Bonnet R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Caubel A, Cheruy F, Codron F, Cozic A, Cugnet D, D’Andrea F, Davini P, de Lavergne C, et al. (2020) Presentation and Evaluation of the IPSL-CM6A-LR Climate Model. Journal of Advances in Modeling Earth Systems 12, e2019MS002010.
Carvalho BM, Rangel EF and Vale MM (2017) Evaluation of the impacts of climate change on disease vectors through ecological niche modelling. Bulletin of Entomological Research 107, 419–430.
Chitimia-Dobler L, Rieß R, Kahl O, Wölfel S, Dobler G, Nava S and Estrada-Peña A (2018) Ixodes inopinatus − Occurring also outside the Mediterranean region. Ticks and Tick-borne Diseases 9, 196–200.
Ciebiera O, Jerzak L, Nowak-Chmura M and Bocheński M (2019) Ticks (Acari: Ixodida) on birds (Aves) migrating through the Polish Baltic coast. Experimental and Applied Acarology 77, 241–251.
Cobos ME, Townsend Peterson A, Barve N. and Osorio-Olvera L (2019) Kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ 7
Cumming GS (2002) Comparing climate and vegetation as limiting factors for species ranges of African ticks. Ecology 83, 255–268.
Cunze S, Glock G, Kochmann J and Klimpel S (2022) Ticks on the move—climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus. Parasitology Research.
Dantas-Torres F (2015) Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect. International Journal for Parasitology: Parasites and Wildlife 4, 452–461.
Dautel H, Kämmer D and Kahl O (2016) 24. How an extreme weather spell in winter can influence vector tick abundance and tick-borne disease incidence. In Braks MAH, Van Wieren SE, Takken W and Sprong H (eds). Ecology and prevention of Lyme borreliosis, pp. 335–349. Brill| Wageningen Academic.
Dunne JP, Horowitz LW, Adcroft AJ, Ginoux P, Held IM, John JG, Krasting JP, Malyshev S, Naik V, Paulot F, Shevliakova E, Stock CA, Zadeh N, Balaji V, Blanton C, Dunne KA, Dupuis C, Durachta J, Dussin R, Gauthier PPG et al. (2020) The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall Coupled Model Description and Simulation Characteristics. Journal of Advances in Modeling Earth Systems 12,e2019MS002015.
Escobar LE (2020) Ecological niche modeling: an introduction for veterinarians and epidemiologists. Frontiers in Veterinary Science 7, 519059.
Estrada-Peña A (2008) Climate, niche, ticks, and models: What they are and how we should interpret them. Parasitology Research 103, 87–95.
Estrada-Pena A and De La Fuente J (2016) Species interactions in occurrence data for a community of tick-transmitted pathogens. Scientific Data 3, 1–13.
Estrada-Peña A, Nava S and Petney T (2014) Description of all the stages of Ixodes inopinatus n. sp. (Acari: Ixodidae). Ticks and Tick-borne Diseases 5, 734–743.
Estrada-Peña A, Alexander N and Wint GRW (2016) Perspectives on modelling the distribution of ticks for large areas: So far so good? Parasites and Vectors 9, 1–10.
Estrada-Peña A, Guglielmone AA and Nava S (2023) Worldwide host associations of the tick genus Ixodes suggest relationships based on environmental sharing rather than on co-phylogenetic events. Parasites & vectors 16, 75.
Feng X, Thompson SE, Woods R and Porporato A (2019) Quantifying asynchronicity of precipitation and potential evapotranspiration in Mediterranean climates. Geophysical Research Letters 46, 14692–14701.
Fick SE and Hijmans RJ (2017) WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International journal of climatology 37, 4302–4315.
Flores-López CA, Moo-Llanes DA, Romero-Figueroa G, Guevara-Carrizales A, López-Ordoñez T, Casas-Martínez M and Samy AM (2022). Potential distributions of the parasite Trypanosoma cruzi and its vector Dipetalogaster maxima highlight areas at risk of Chagas disease transmission in Baja California Sur, Mexico, under climate change. Medical and Veterinary Entomology 36, 469–479.
Garcia-Vozmediano A, Krawczyk AI, Sprong H, Rossi L, Ramassa, E. and Tomassone, L. (2020). Ticks climb the mountains: Ixodid tick infestation and infection by tick-borne pathogens in the Western Alps. Ticks and Tick-borne Diseases 11, 101489.
Gilbert L (2021) The impacts of climate change on ticks and tick-borne disease risk. Annual Review of Entomology 66, 273–288.
Gilbert L, Aungier J and Tomkins JL (2014) Climate of origin affects tick ( Ixodes ricinus ) host‐seeking behavior in response to temperature: implications for resilience to climate change? Ecology and Evolution 4, 1186–1198.
Ginsberg HS, Hickling GJ, Pang G, Tsao JI, Fitzgerald M, Ross B, Rulison EL and Burke RL (2022) Selective host attachment by Ixodes scapularis (acari: Ixodidae): tick–lizard associations in the southeastern United States. Journal of Medical Entomology 59, 267–272.
Giorgi F and Lionello P (2008) Climate change projections for the Mediterranean region. Global and Planetary Change 63, 90–104.
Gray JS and Ogden NH (2021) Ticks, human babesiosis and climate change. Pathogens 10, 1430.
Gray JS, Kahl O, Lane RS, Levin ML and Tsao JI (2016) Diapause in ticks of the medically important Ixodes ricinus species complex. Ticks and Tick-borne Diseases 7, 992–1003.
Gray J, Kahl O and Zintl A (2021) What do we still need to know about Ixodes ricinus? Ticks and Tick-borne Diseases 12, 101682.
Hauck D, Springer A, Pachnicke S, Schunack B, Fingerle V and Strube C (2019) Ixodes inopinatus in northern Germany: occurrence and potential vector role for Borrelia spp., Rickettsia spp., and Anaplasma phagocytophilum in comparison with Ixodes ricinus. Parasitology Research 118, 3205–3216.
Hauser G, Rais O, Morán Cadenas F, Gonseth Y, Bouzelboudjen M and Gern L (2018) Influence of climatic factors on Ixodes ricinus nymph abundance and phenology over a long-term monthly observation in Switzerland (2000–2014). Parasites & Vectors 11, 289.
Hausfather Z (2020) CMIP6: The Next Generation of Climate Models Explained—Carbon Brief. Available at https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained/
Hekimoğlu O (2022) Phylogenetic placement of Turkish populations of Ixodes ricinus and Ixodes inopinatus. Experimental and Applied Acarology 88, 179–189.
Hoerling M, Eischeid J, Perlwitz J, Quan X, Zhang T and Pegion P (2012) On the increased frequency of Mediterranean drought. Journal of Climate 25, 2146–2161.
Hofmeester TR, Coipan EC, Van Wieren SE, Prins HHT, Takken W and Sprong H (2016) Few vertebrate species dominate the Borrelia burgdorferi s.l. life cycle. Environmental Research Letters 11, 043001.
Hvidsten D, Frafjord K, Gray JS, Henningsson AJ, Jenkins A, Kristiansen BE, Lager M, Rognerud B, Slåtsve AM, Stordal F, Stuen S and Wilhelmsson P (2020) The distribution limit of the common tick, Ixodes ricinus, and some associated pathogens in north-western Europe. Ticks and Tick-borne Diseases 11, 101388.
Jaenson TGT and Lindgren E (2011) The range of Ixodes ricinus and the risk of contracting Lyme borreliosis will increase northwards when the vegetation period becomes longer. Ticks and Tick-borne Diseases 2, 44–49.
Karger DN, Lange S, Hari C, Reyer CPO, Conrad O, Zimmermann NE and Frieler K (2022) CHELSA-W5E5: Daily 1 km meteorological forcing data for climate impact studies. Earth System Science Data Discussions 15, 1–28.
Kilpatrick AM and Randolph SE (2012) Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. The Lancet 380, 1946–1955.
Knülle W and Rudolph D In Obenchain FD and Galun R (eds). Physiology of Ticks. Oxford,
UK: Pergamon Press. doi: 10.1016/b978-0-08-024937-7.50007-x
Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H et al. (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions 19, 1366–1379.
Krasnov BR, Stanko M and Morand S (2007) Host community structure and infestation by ixodid ticks: Repeatability, dilution effect and ecological specialization. Oecologia 154, 185–194.
Krawczyk AI, Röttjers L, Fonville M, Takumi K, Takken W, Faust K and Sprong H (2022) Quantitative microbial population study reveals geographical differences in bacterial symbionts of Ixodes ricinus. Microbiome 10, 120.
Lauterbach R, Wells K, O’Hara RB, Kalko EKV and Renner SC (2013) Variable strength of forest stand attributes and weather conditions on the questing activity of Ixodes ricinus ticks over years in managed forests. PLoS ONE 8, e55365.
Lembrechts JJ, Nijs I and Lenoir J (2019) Incorporating microclimate into species distribution models. Ecography 42, 1267–1279.
Lembrechts JJ, Aalto J, Ashcroft MB, De Frenne P, Kopecký M, Lenoir J, Luoto M, Maclean IMD, Roupsard O, Fuentes-Lillo E, García RA, Pellissier L, Pitteloud C, Alatalo JM, Smith SW, Björk RG, Muffler L, Ratier Backes A, Cesarz S, Gottschall F. et al. (2020) SoilTemp: A global database of near-surface temperature. Global Change Biology 26, 6616–6629.
Li S, Heyman P, Cochez C, Simons L and Vanwambeke SO (2012) A multi-level analysis of the relationship between environmental factors and questing Ixodes ricinus dynamics in Belgium. Parasites and Vectors 5, 1-11.
Lindgren E, Tälleklint L and Polfeldt T (2000) Impact of climatic change on the northern latitude limit and population density of the disease-transmitting European tick Ixodes ricinus. Environmental Health Perspectives 108, 119–123.
Liu C, Berry PM, Dawson TP and Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393.
Maclean IMD, Duffy JP, Haesen S, Govaert S, De Frenne P, Vanneste T, Lenoir J, Lembrechts JJ, Rhodes MW and Van Meerbeek K (2021) On the measurement of microclimate. Methods in Ecology and Evolution 2021, 1–14.
Marcin M, Raschmanová N, Miklisová D and Kováč Ľ (2021) Microclimate and habitat heterogeneity as important drivers of soil Collembola in a karst collapse doline in the temperate zone. Invertebrate Biology 140, e12315.
Martello E, Mannelli A, Ragagli C, Ambrogi C, Selmi M, Ceballos LA and Tomassone L (2014) Range expansion of Ixodes ricinus to higher altitude, and co-infestation of small rodents with Dermacentor marginatus in the Northern Apennines, Italy. Ticks and Tick-borne Diseases 5, 970–974.
Materna J, Daniel M, Metelka L and Harčarik J (2008) The vertical distribution, density and the development of the tick Ixodes ricinus in mountain areas influenced by climate changes (The Krkonoše Mts., Czech Republic). International Journal of Medical Microbiology 298, 25–37.
Mauritsen T, Bader J, Becker T, Behrens J, Bittner M, Brokopf R, Brovkin V, Claussen M, Crueger T, Esch M, Fast I, Fiedler S, Fläschner D, Gayler V, Giorgetta M, Goll DS, Haak H, Hagemann S, Hedemann C, Hohenegger C et al. (2019) Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2. Journal of Advances in Modeling Earth Systems 11, 998–1038.
Medlock JM, Pietzsch ME, Rice NVP, Jones L, Kerrod E, Avenell D, Los S, Ratcliffe N, Leach S and Butt T (2008) Investigation of ecological and environmental determinants for the presence of questing Ixodes ricinus (Acari: Ixodidae) on Gower, South Wales. Journal of Medical Entomology 45, 314–325.
Moo-Llanes DA, de Oca-Aguilar ACM, Romero-Salas D and Sánchez-Montes S (2021) Inferring the potential distribution of an emerging rickettsiosis in America: The case of Rickettsia parkeri. Pathogens 10, 592.
Naimi B (2017) Package ‘usdm’. Uncertainty analysis for species distribution models. Available at https://cran.r-project.org/web/packages/usdm/
Naumann G, Alfieri L, Wyser K, Mentaschi L, Betts RA, Carrao H, Spinoni J, Vogt J and Feyen L (2018) Global changes in drought conditions under different levels of warming. Geophysical Research Letters 45, 3285–3296.
Nava S and Guglielmone AA (2013) A meta-analysis of host specificity in Neotropical hard ticks (Acari: Ixodidae). Bulletin of Entomological Research 103, 216–224.
Noll M, Wall R, Makepeace BL, Newbury H, Adaszek L, Bødker R, Estrada-Peña A, Guillot J, Da Fonseca IP, Probst J, Overgaauw P, Strube C, Zakham F, Zanet S and Rose Vineer H (2023) Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches. Parasites & Vectors 16, 384.
Nolzen H, Brugger K, Reichold A, Brock J, Lange M and Thulke HH (2022) Model-based extrapolation of ecological systems under future climate scenarios: The example of Ixodes ricinus ticks. PLoS ONE 17, e0267196.
Nuñez-Penichet C, Osorio-Olvera L, Gonzalez VH, Cobos ME, Jiménez L, DeRaad DA, Alkishe A, Contreras-Díaz RG, Nava-Bolaños A and Utsumi K (2021) Geographic potential of the world’s largest hornet, Vespa mandarinia Smith (Hymenoptera: Vespidae), worldwide and particularly in North America. PeerJ 9, e10690.
Open Source Geospatial Foundation (2022) GDAL, Geospatial Data Abstraction Software Library. Available at https://gdal.org/
Oshima N, Yukimoto S, Deushi M, Koshiro T, Kawai H, Tanaka TY and Yoshida K (2020) Global and Arctic effective radiative forcing of anthropogenic gases and aerosols in MRI-ESM2.0. Progress in Earth and Planetary Science 7,1-21.
Ostfeld RS and Brunner JL (2015) Climate change and Ixodes tick-borne diseases of humans. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 1–11.
Owens HL, Campbell LP, Dornak LL, Saupe EE, Barve N, Soberón J, Ingenloff K, Lira-Noriega A, Hensz CM, Myers CE and Peterson AT (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263, 10–18.
Perret JL, Rais O and Gern L (2004) Influence of climate on the proportion of Ixodes ricinus Nymphs and adults questing in a tick population. Journal of Medical Entomology 41, 361–365.
Peterson AT and Soberón J (2012) Species distribution modeling and ecological niche modeling: getting the concepts right. Natureza a Conservacao 10, 102–107.
Peterson AT, Cobos ME and Jiménez-García D (2018) Major challenges for correlational ecological niche model projections to future climate conditions. Annals of the New York Academy of Sciences 1429, 66–77.
Phillips SJ, Anderson RP and Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231–259.
Porretta D, Mastrantonio V, Amendolia S, Gaiarsa S, Epis S, Genchi C, Bandi C, Otranto D and Urbanelli S (2013) Effects of global changes on the climatic niche of the tick Ixodes ricinus inferred by species distribution modelling. Parasites and Vectors 6, 2–9.
QGIS Geographic Information System (2022) QGIS Association. Available at https://www.qgis.org
R Core Team (2022) R: A Language and Environment for Statistical Computing (4.22). R Foundation for Statistical Computing. Vienna, Austria. Available at https://www.r-project.org/
Raghavan RK, Heath ACG, Lawrence KE, Ganta RR, Peterson AT and Pomroy WE (2020) Predicting the potential distribution of Amblyomma americanum (Acari: Ixodidae) infestation in New Zealand, using maximum entropy-based ecological niche modelling. Experimental and Applied Acarology 80, 227–245.
Randolph SE and Storey K (1999) Impact of microclimate on immature tick-rodent host interactions (Acari: Ixodidae): Implications for parasite transmission. Journal of Medical Entomology 36, 741–748.
Rocklöv J and Dubrow R (2020). Climate change: an enduring challenge for vector-borne disease prevention and control. Nature Immunology 21, 479–483.
Rollins RE, Margos G, Brachmann A, Krebs S, Mouchet A, Dingemanse NJ, Laatamna A, Reghaissia N, Fingerle V, Metzler D, Becker NS and Chitimia-Dobler L (2023) German Ixodes inopinatus samples may not actually represent this tick species. International Journal for Parasitology 53, 751–761.
Ruiz-Fons F, Fernández-de-Mera IG, Acevedo P, Gortázar C and De La Fuente J (2012) Factors driving the abundance of Ixodes ricinus Ticks and the prevalence of zoonotic I. ricinus-borne pathogens in natural foci. Applied and Environmental Microbiology 78, 2669–2676.
Schwarz A, Hönig V, Vavrušková Z, Grubhoffer L, Balczun C, Albring A. and Schaub GA (2012) Abundance of Ixodes ricinus and prevalence of Borrelia burgdorferi s.l. in the nature reserve Siebengebirge, Germany, in comparison to three former studies from 1978 onwards. Parasites & Vectors 5, 268.
Sellar AA, Jones CG, Mulcahy JP, Tang Y, Yool A, Wiltshire A, O’Connor FM, Stringer M, Hill R, Palmieri J, Woodward S, de Mora L, Kuhlbrodt T, Rumbold ST, Kelley DI, Ellis R, Johnson CE, Walton J, Abraham NL, Andrews MB et al. (2019) UKESM1: Description and Evaluation of the U.K. Earth System Model. Journal of Advances in Modeling Earth Systems 11, 4513–4558.
Semenza JC and Suk JE (2018) Vector-borne diseases and climate change: A European perspective. FEMS Microbiology Letters 365, 1–9.
Soberon J and Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics 2,1-10.
Stark JR and Fridley JD (2022) Microclimate‐based species distribution models in complex forested terrain indicate widespread cryptic refugia under climate change. Global Ecology and Biogeography 31, 562–575.
Steven JP, Miroslav D and Schapire RE (2021) MaxEnt software for modeling species niches and distributions, version 3.4.4. Available at
https://biodiversityinformatics.amnh.org/open_source/maxent/
Tagliapietra V, Rosà R, Arnoldi D, Cagnacci F, Capelli G, Montarsi F, Hauffe HC and Rizzoli A (2011) Saturation deficit and deer density affect questing activity and local abundance of Ixodes ricinus (Acari, Ixodidae) in Italy. Veterinary Parasitology 183, 114–124.
Ulbrich U, May W, Li L, Lionello P, Pinto JG and Somot S (2006) The Mediterranean climate change under global warming. In Lionello P, Malanotte-Rizzoli P and Boscolo R (eds). Developments in Earth and Environmental Sciences. Amsterdam, Netherlands: Elsevier, pp. 399–415.
Van Gestel M, Matthysen E, Heylen D and Verheyen K (2022). Survival in the understorey: Testing direct and indirect effects of microclimatological changes on Ixodes ricinus. Ticks and Tick-borne Diseases 13, 102035.
Volk MR, Lubelczyk CB, Johnston JC, Levesque DL and Gardner AM (2022) Microclimate conditions alter Ixodes scapularis (Acari: Ixodidae) overwinter survival across climate gradients in Maine, United States. Ticks and Tick-borne Diseases 13, 101872.
Waldenström J, Lundkvist Å, Falk KI, Garpmo U, Bergström S, Lindegren G, Sjöstedt A, Mejlon H, Fransson T, Haemig PD and Olsen B (2007) Migrating birds and Tickborne Encephalitis Virus. Emerging Infectious Diseases 13, 1215–1218.
Warren DL and Seifert SN (2011) Ecological niche modeling in MaxEnt: the importance of model complexity and the performance of model selection criteria. Ecological Applications 21, 335–342.
Younsi H, Fares W, Cherni S, Dachraoui K, Barhoumi W, Najjar C and Zhioua E (2020) Ixodes inopinatus and Ixodes ricinus (Acari: Ixodidae) are sympatric ticks in North Africa. Journal of Medical Entomology 57, 952–956.
Zhang Y, Zhang X and Liu J (2019) Ticks (Acari: Ixodoidea) in China: Geographical distribution, host diversity, and specificity. Archives of Insect Biochemistry and Physiology 102, e21544.
Zhao GP, Wang YX, Fan ZW, Ji Y, Liu MJ, Zhang WH, Li XL, Zhou SX, Li H, Liang S, Liu W, Yang Y and Fang LQ (2021) Mapping ticks and tick-borne pathogens in China. Nature Communications 12, 1–13.

The authors declare no competing interests.

Download PDF

Version 1

posted

You are reading this latest preprint version

Predicting the distribution of Ixodes ricinus in Europe: Integrating microclimatic factors into ecological niche models

Status:

Version 1

Abstract

Figures

Key Findings

Introduction

Material and Methods

Species distribution data

Environmental variables

Future Projections

Ecological Niche Models

Results

Model Parameters

Current and Future Predictions

Uncertainty and MOP results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1