High-resolution prediction models for Rhipicephalus microplus and Amblyomma cajennense s.l. ticks affecting cattle and their spatial distribution in continental Ecuador using bioclimatic factors

doi:10.21203/rs.3.rs-3234801/v1

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Research Article

High-resolution prediction models for Rhipicephalus microplus and Amblyomma cajennense s.l. ticks affecting cattle and their spatial distribution in continental Ecuador using bioclimatic factors

https://doi.org/10.21203/rs.3.rs-3234801/v1

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published 23 Feb, 2024

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In Ecuador, the main tick species affecting cattle are Rhipicephalus microplus and Amblyomma cajennense sensu lato. Understanding their spatial distribution is crucial. To assess their distribution, data from 2895 farms visited between 2012 and 2017 were utilized. Ticks were collected during animal inspections, with each farm's location georeferenced. Bioclimatic variables and vapor pressure deficit data from CHELSA were overlaid to develop predictive maps for each species using Random Forest (RF) models. The cross-validation results for RF prediction models showed high accuracy for both R. microplus and A. cajennense s.l. presence with values of Accuracy = 0.97 and 0.98, Sensitivity = 0.96 and 0.99, and Specificity = 0.96 and 0.93, respectively. A carefully selected subset of bioclimatic variables was used to describe the presence of each tick species. Higher levels of precipitation had positive effect on the presence of R. microplus but a negative effect on A. cajennense s.l. In contrast, isothermality (BIO3) resulted as more important for the presence of A. cajennense s.l. compared to R. microplus. As a result, R. microplus had a broader distribution across the country, while A. cajennense s.l. was mainly found in coastal areas with evident seasonality. The coexistence of both species in certain regions could be attributed to transitional zones, whereas high altitudes limited tick presence. This information can aid in developing appropriate tick management plans, particularly considering A. cajennense s.l.'s broad host range species and R. microplus's specificity for cattle. Moreover, the predictive models can identify areas at risk of associated challenging hemoparasite, requiring special attention and mitigation measures.

Distribution model

suitability

bioclimatic

cattle ticks

Ecuador

Rhipicephalus microplus

Amblyomma cajennense s.l

Ecuador is a megadiverse country with particular climatic characteristics due to the Andes mountain range, with tropical and subtropical zones where around 70% of livestock activities take place (Guillén and Muñoz 2013). The agricultural sector is important for the economy, accounting for 7.7% of the gross domestic product (Primicias 2022). However, these areas have also been affected by the presence of ticks. Hard ticks of the Ixodidae family affect mammals. Cattle are frequently affected by ticks in Ecuador, as are humans in frequently incidental cases. These ectoparasites are important given the economic losses they cause in the livestock sector (Paucar-Quishpe et al. 2023)

The main species affecting cattle in Ecuador are Rhipicephalus microplus and Amblyomma cajennense s.l. (Bustillos and Rodríguez 2016; Maya-Delgado et al. 2020; Paucar-Quishpe et al. 2022). R. microplus is widely distributed in tropical and subtropical areas of Australia, Africa, and Latin America (doubtfully established in Chile) (González-Acuña and Guglielmone 2005). It arrived around five centuries ago with the Spanish colonizers (Estrada-Peña 1999) and adapted well to Ecuador. A. cajennense s.l. is distributed from the southern USA to northern Argentina, and it is a multi-host ectoparasite in mammals including humans incidentally. These species are not satisfactorily controlled in many countries (Lima et al. 2000; Forero-Becerra et al. 2022) and cause direct and indirect losses (Alcala-Canto et al. 2018). In cattle, parasitism results in weight loss, reduction in milk production (Estrada-Peña et al. 2006b) estimated at around 90.2 liters per cow per year (Marques et al. 2020), and weakness due to blood loss. For the farmers, costs are associated to control and morbidity as well as animal mortality (Alonso-Díaz et al. 2013). In addition, Paucar-Quishpe et al. (2023) associated cattle financial losses with tick acaricide-resistances in Ecuador.

Cattle ticks depend on climatic and management conditions for their survival and development. Various factors, such as rainfall and temperature, directly influence the number of generations per year. The most limiting factors for their presence, as well as the tick ecotype, can vary between regions (Lima et al. 2000; Estrada-Peña et al. 2006b). While the presence of a host is necessary, it is not considered a determining factor for tick development. At broad scales, climate factors have been found useful to delimit ticks distribution (Estrada-Peña et al. 2006a, b). Ticks also vector pathogens that affect cattle; R. microplus is an efficient vector for Babesia bovis, and Babesia bigemina, and a suspected vector for Anaplasma spp. in Ecuador (Escobar et al. 2015; Insuaste Taipe 2021), causing emaciation in cattle and even death (Pothmann et al. 2016; Kasaija et al. 2021). A. cajennense s.l. is also known to transmits the bacterium Ehrlichia ruminantium, which has not yet been reported in Ecuador.

To develop adequate control plans, it is necessary to understand the geographical distribution, and suitability of ticks (Estrada-Peña 1999). Although both species affect cattle, their biology differs. Tick R. microplus fulfills its biological cycle on one host, and has high host specificity for cattle (Nava et al. 2013) while A. cajennense s.l. fulfills its cycle on three hosts, infesting mainly equines (Alonso-Díaz et al. 2013). However, on-farm control often does not account for tick biology, which can lead to a poor efficacy and can induce acaricide resistance which has been widely reported for R. microplus (Bianchi et al. 2003; Maya-Delgado et al. 2020; Dzemo et al. 2022; Paucar-Quishpe et al. 2023). Few studies have been conducted for A. cajennense s.l. but the same problem is already evident (Alonso-Díaz et al. 2013).

Various distribution models have been published in the past for R. microplus in South America and West Africa (Estrada-Peña 1999; Estrada-Peña et al. 2006c; De Clercq et al. 2015; Zannou et al. 2022). They mostly use data drawn from bibliographic sources that often do not represent well the Andes, and the particular climatological conditions present in Ecuador. Estrada-Peña (1999) showed absence of R. microplus suitability in Ecuador, Marques et al. (2020) in their distribution model of R. microplus found a high suitability in the Andean zone of Ecuador, and Estrada-Peña et al. (2005) showed suitability in few parts of the Coastal zone. As for A. cajennense s.l. there are few distribution prediction studies in South América including Ecuador. Aguilar-Domínguez et al. (2021) showed suitability in the occidental part of the Coastal zone of Ecuador for A. mixtum, similar than Estrada-Peña et al. (2014).

Thus, the present study aims to update the distribution cattle ticks (R. microplus and A. cajennense s.l.) in Ecuador, considering that their presence in highlands remain rare (Chávez-Larrea et al. 2021), and international databases may not adequately capture the specificities of mountain conditions in the Andes. Additionally, the effects of climate change and global warming could potentially alter the current spatial structure of both tick species in the future. Therefore, the main objective of this study is to use an extensive national dataset concerning the presence and absence of ticks on cattle (R. microplus and A. cajennense s.l.) and plugging it to the spatial bioclimatic variables measured to determine their distribution. Furthermore, the study aims to predict suitable areas where these ticks can affect cattle in continental Ecuador. The findings of this study will provide valuable information for future prevention and control plans.

Study area

A cross-sectional study collected from another cross-sectional studies carried out by the Instituto de Investigación en Zoonosis (CIZ) in Universidad Central del Ecuador (UCE): between 2012 and 2017. To mentioned the following studies: the “National survey about bovine Brucellosis, tuberculosis, and cattle ticks” (Maya-Delgado et al. 2020; Paucar et al. 2021), the “Spatial analysis and ecological-epidemiological aspects of Rhipicephalus microplus infestation and its resistance to acaricides”, the “Wild arthropod vectors and domestic reservoirs as indicators of vulnerability to re-emerging zoonotic diseases in the Ecuadorian Amazon", and the "Molecular epidemiology of parasites and microorganisms of zoonotic interest: cattle screwworm and ticks". Altogether, ticks were sampled from 22 of the 23 Ecuador continental provinces. In each farm randomly three bovines were sampled for ticks. So far 2895 cattle farms were visited in the three continental regions of the country. In the Coastal (n = 7 provinces, 100%), the Andean (n = 9 provinces, 90%), and the Amazon (n = 6 provinces, 100%). Table 1 and Fig. 1 describe the sampling in the provinces. Geographical coordinates were recorded in each farm with a Garmin GPS (WGS 84).

Table 1

Number of farms sampled in each province.
REGION	PROVINCES	PROVINCES NUMBER	FARMS
Andean	Azuay	01	142
Andean	Bolivar	02	241
Andean	Carchi	04	81
Andean	Chimborazo	06	276
Andean	Cotopaxi	05	220
Andean	Imbabura	10	144
Andean	Loja	11	332
Andean	Pichincha	17	127
Andean	Tungurahua	18	199
Coastal	El Oro	07	86
Coastal	Esmeraldas	08	98
Coastal	Guayas	09	137
Coastal	Los Rios	12	110
Coastal	Manabí	13	312
Coastal	Santa Elena	24	17
Coastal	Sto. Domingo De Los Tsáchilas	23	111
Amazon	Morona Santiago	14	7
Amazon	Napo	15	79
Amazon	Orellana	22	9
Amazon	Pastaza	16	52
Amazon	Sucumbíos	21	30
Amazon	Zamora Chinchipe	19	85
Total			2895

Tick collection and morphological identification

The ticks collected were stored in tubes with absolute ethanol (100%) and taken to the Unidad de Entomología Aplicada of Instituto de Investigación en Zoonosis (CIZ). Ticks were morphologically identified using an Olympus SZ51 stereomicroscope with magnifications ×0.8–4.5 and taxonomic keys (Guerrero 1996; Voltzit 2007; Nava et al. 2014).Ticks were identified to genus and species level. We focused on R. microplus and A. cajennense s. l., as they were the most abundant (Rodríguez-Hidalgo et al. 2017; Maya-Delgado et al. 2020; Paucar et al. 2022).

Ethical considerations

The attainment of the study's objectives did not necessitate approval from research ethics committees, as the experimental procedures involved an unregulated invertebrate species.

Bioclimatic data

We used the 19 bioclimatic layers from Chelsa Bioclim, derived from the monthly mean, max, mean temperature, and mean precipitation values and the vapor pressure deficit from 1981–2010 (Table 2). This information is available at a horizontal resolution of 30 arc sec. Although relative humidity has been commonly used, vapor pressure deficit is a more useful parameter for evaluating environmental conditions in relation to tick development, as it accounts for the air's drying power (Wollaeger and Runkle 2015; Pascoe et al. 2019; Estrada-Peña 2023).The area of continental Ecuador was extracted using the “mask” function from raster package under R environment. Each bioclimatic raster variable, as Chelsa recommends, was multiplied for the scale value and then added to the offset value (Karger et al. 2017).

Table 2

Bioclimatic variables obtained from CHELSEA
Short name	Long name	Unit	Explanation
Bio1	Mean annual air temperature	°C	Mean annual daily mean air temperatures averaged over 1 year
Bio2	Mean diurnal air temperature range	°C	Mean diurnal range of temperatures averaged over 1 year
Bio3	Isothermality	°C	Ratio of diurnal variation to annual variation in temperatures
Bio4	Temperature seasonality	°C/100	Standard deviation of the monthly mean temperatures
Bio5	Mean daily maximum air temperature of the warmest month	°C	Highest temperature of any monthly daily mean maximum temperature
Bio6	Mean daily minimum air temperature of the coldest month	°C	Lowest temperature of any monthly daily mean maximum temperature
Bio7	Annual range of air temperature	°C	Difference between the Maximum Temperature of warmest month and the minimum temperature of coldest month
Bio8	Mean daily mean air temperatures of the wettest quarter	°C	Wettest quarter of the year is determined (to the nearest month)
Bio9	Mean daily mean air temperatures of the driest quarter	°C	Driest quarter of the year is determined (to the nearest month)
Bio10	Mean daily mean air temperatures of the warmest quarter	°C	Warmest quarter of the year is determined (to the nearest month)
Bio11	Mean daily mean air temperatures of the coldest quarter	°C	Coldest quarter of the year is determined (to the nearest month)
Bio12	Annual precipitation amount	kg m-2 year-1	Accumulated precipitation amount over 1 year
Bio13	Precipitation amount of the wettest month	kg m-2 month-1	Precipitation of the wettest month.
Bio14	Precipitation amount of the driest month	kg m-2 month-1	Precipitation of the driest month.
Bio15	Precipitation seasonality	kg m-2	Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean)
Bio16	Mean monthly precipitation amount of the wettest quarter	kg m-2 month-1	Wettest quarter of the year is determined (to the nearest month)
Bio17	Mean monthly precipitation amount of the driest quarter	kg m-2 month-1	Driest quarter of the year is determined (to the nearest month)
Bio18	Mean monthly precipitation amount of the warmest quarter	kg m-2 month-1	Warmest quarter of the year is determined (to the nearest month)
Bio19	Mean monthly precipitation amount of the coldest quarter	kg m-2 month-1	Coldest quarter of the year is determined (to the nearest month)
VPD_max	Maximum monthly vapor pressure deficit	Pa	Highest monthly vapor pressure deficit
VPD_mean	Mean monthly vapor pressure deficit	Pa	Average monthly vapor pressure deficit over 1 year
VPD_min	Minimum monthly vapor pressure deficit	Pa	Lowest monthly vapor pressure deficit

Correlative analysis.

In order to identify the strongest predictors of tick presence, generalized linear models (GLM) analyses were conducted using presence and absence data on R. microplus, and A. cajennense s.l., and as explanatory variables, the climatology data Bio1 to Bio19 and the vapor pressure deficit mean, minimum, and maximum. For the multivariable analyses, we included the explanatory variables with a P value < 0.2 in the univariate analyses. Effect of collinearity in the model was reduced by removing variables presenting a variance inflation factor (VIF) higher than 8, with the exception of the variables known from literature to be important in tick biology. Since all variables are derived from temperature and precipitation, they are expected to be collinear with each other. Forward stepwise selection was used then to build the final multiple GLM model, with the stepAIC function into “MASS” package. We used the subset of variables producing the lowest Akaike information criteria (AIC) to have the best performing model. Finally, we computed the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC-ROC) of the model, using the “CARET” and “Proc” R packages. The bioclimatic variables were standardized using “scale” function in R.

Predictive Models for cattle tick’s habitat suitability.

Random forest, a supervised model, was used to model habitat suitability of cattle ticks (Kopsco et al. 2022; Zannou et al. 2022). Using the presence and absence data and the results of the multiple GLM, the habitat suitability prediction model in continental Ecuador under bioclimatic conditions was modelled using the “Random Forest” package in R.

Random Forest yields a value from 0 (completely unsuitable) to 1 (fully suitable). Using the "importance" function in the “Random Forest” package, we calculated the Mean Square Error (MSE), revealing elevated percentages for the most significant variables within the resulting Random Forest model. The models were evaluated using sensitivity and specificity and area under the ROC curve (AUC-ROC). The models were trained with 80% of the data (randomly selected), and validated with the other 20% for 10 times, to obtain the mean sensitivity, specificity, and accuracy. Models were adjusted for: R. microplus, and A. cajennense s.l. The best model for each was mapped in QGIS, and reclassified into 5 categories: 0 to 0.2 (probability very low); 0.2 to 0.4 (probability low); 0.4 to 0.6 (moderate); 0.6 to 0.8 (high) and 0.8 to 1 (very high) (Namgyal et al. 2021).

In order to mask out areas recently not used for agriculture, we use the land cover map of Ecuador (Ministerio de Agricultura y Ganadería [MAG] 2014) as in Table 3. This data is from 2014 but is the most recent version. As scenario, we used a 50% transparency mask so that areas that may become used for cattle raising in the future are included even though prediction accuracy may be lower in unsampled areas.

Table 3

Columns of land use Ecuador shape use to overlaid to Random Forest models.
Land use	Included or excluded
Agricultural	Excluded
Anthropic	Included
Agricultural association	Excluded
Forest	Included
Water bodies	Included
Other areas	Included
Shrub vegetation	Included
Farming	Included
Farming conservation	Included
Forest agriculture and farming	Excluded
Mixed agriculture and farming	Excluded
Conservation and protection	Included
Forestry	Included
Livestock	Excluded
Livestock conservation	Excluded
Unproductive land	Included

Finally, we made a map of the probability of having both species by combining the suitability maps of R. microplus and A. cajennense s.l. Suitability values were reclassified as: 0 to 0.5 = 0, and 0.51 to 1 = 1. The reclassified maps were combined in a new raster with the following classes: 0 = no ticks, 1 = one tick species, and 2 = two tick species.

Presence data

The number of included farms were 2895, of which 1780 farms had R. microplus presence. 460 farms had A. cajennense s.l., and 378 both species (Table 4). The location in space of the Ecuadorian provinces could be viewed in Fig. 1.

Table 4

Presence and absence of *R. microplus*, and *A.cajennense* s.l., in cattle farms per province.
Provinces		Farms sampled		R. microplus								A. cajennense s.l.
Provinces		Farms sampled		Absence				Presence				Absence				Presence
		#		#		%		#		%		#		%		#		%
Azuay		142		142		100		0		0		142		100		0		0
Bolivar		241		196		81		45		19		234		97		7		3
Carchi		81		72		89		9		11		80		99		1		1
Chimborazo		276		270		98		6		2		276		100		0		0
Cotopaxi		220		194		88		26		12		214		97		6		3
Imbabura		144		105		73		39		27		142		99		2		1
Loja		332		168		51		164		49		269		81		63		19
Pichincha		127		62		49		65		51		127		100		0		0
Tungurahua		199		195		98		4		2		199		100		0		0
El Oro		86		15		17		71		83		56		65		30		35
Esmeraldas		98		40		41		58		59		24		24		74		76
Guayas		137		54		39		83		61		98		72		39		28
Los Rios		110		32		29		78		71		71		65		39		35
Manabi		312		112		36		200		64		150		48		162		52
Santa Elena		17		8		47		9		53		8		47		9		53
Sto. Domingo		111		14		13		97		87		85		77		26		23
Morona Santiago		7		0		0		7		100		7		100		0		0
Napo		79		21		27		58		73		79		100		0		0
Orellana		9		0		0		9		100		9		100		0		0
Pastaza		52		51		98		1		2		52		100		0		0
Sucumbios		30		2		7		28		93		30		100		0		0
Zamora Chinchipe		85		27		32		58		68		83		98		2		2
Total	2895		1780		61		1115		39		2435		84		460		16

Distribution maps

The distribution of R. microplus and A. cajennense s.l. is presented in Figs. 2 and 3. Farms with presence of one and both are shown in Fig. 4. R. microplus is widely distributed in the Coastal, foothills of the Andean zone and Amazon. A. cajennense s.l. is distributed in the Coastal zone and in the western foothills of the Andean zone.

co-occurrence at farm level in continental Ecuador.

The points were jittered to avoid overlap

Bi-variable generalized linear models

The individual association between bioclimatic factors and the presence of R. microplus and A. cajennense s.l. showed P values lower than 0.2 for all variables tested (Table 5).

Table 5

P value and Odds ratio for the bi variable GLM of *R. microplus* and *A. cajennense* s.l. in association with bioclimatic variables obtained from Chelsa.
Variables		R. microplus			A. cajennense s.l.
Variables		P value		ODDS ratio	P value		ODDS ratio
Bio	1	< 0.01	***	1.2694	< 0.01	***	1.4823
Bio	2	< 0.01	***	0.5649	< 0.01	***	0.5965
Bio	3	< 0.01	***	0.9423	< 0.01	***	0.9681
Bio	4	< 0.01	***	1.0076	< 0.01	***	1.0123
Bio	5	< 0.01	***	1.2966	< 0.01	***	1.5575
Bio	6	< 0.01	***	1.2429	< 0.01	***	1.4146
Bio	7	< 0.01	***	0.6338	< 0.01	***	0.6376
Bio	8	< 0.01	***	1.2568	< 0.01	***	1.4602
Bio	9	< 0.01	***	1.2799	< 0.01	***	1.4819
Bio	10	< 0.01	***	1.2546	< 0.01	***	1.4409
Bio	11	< 0.01	***	1.2811	< 0.01	***	1.5295
Bio	12	< 0.01	***	1.0003	< 0.01	***	0.9997
Bio	13	< 0.01	***	1.0037	< 0.01	***	1.0010
Bio	14	< 0.01	**	0.9983	< 0.01	***	0.9636
Bio	15	< 0.01	***	1.0237	< 0.01	***	1.0456
Bio	16	< 0.01	***	1.0012	< 0.01	**	1.0003
Bio	17	0.06	.	0.9997	< 0.01	**	1.0003
Bio	18	< 0.01	***	1.0011	< 0.01	**	1.0003
Bio	19	0.09	.	0.9997	< 0.01	***	0.9914
VPD	max	< 0.01	***	1.0039	< 0.01	***	1.0051
VPD	min	< 0.01	***	1.0045	< 0.01	***	1.0060
VPD	mean	< 0.01	***	1.0043	< 0.01	***	1.0057

P value 0=***, 0.001= **, 0.01= * 0.05=.

Multivariate generalized linear model

The final explanatory GLM after remove the variables with high collinearity, and with the lowest AIC for R. microplus and A. cajennense s.l., included 10 bioclimatic variables: Bio1, Bio2, Bio3, Bio4, Bio12, Bio13, Bio14, Bio18, VPD_max, and VPD_min (Table 6).

Table 6

Multivariate generalized linear models of the presence of cattle ticks, *R. microplus*, and *A. cajennense* s.l. and their association with bioclimatic factors
Models		R. microplus			A. cajennense s.l.
Variables		P value		ODDS ratio	P value		ODDS ratio
Bio	1	< 0.01	***	39.38 (24.09–65.34)	< 0.01	***	359.30 (138.17-1014.14)
Bio	2	< 0.01	***	1.65 (1.35–2.02)	< 0.01	***	1.95 (1.49–2.55)
Bio	3	< 0.01	***	0.65 (0.55–0.77)	< 0.01	***	0.53 (0.41–0.68)
Bio	4	< 0.01	***	0.63(0.53–0.74)	< 0.01	***	0.55 (0.44–0.69)
Bio	12	< 0.01	***	8.10 (3.80-17.41)	< 0.01	***	17.14 (4.14–72.24)
Bio	13	< 0.01	**	0.43 (0.244–0.75)	< 0.01	***	0.15 (0.05–0.38)
Bio	14	< 0.01	***	0.35 (0.22–0.53)	< 0.01	***	0.06 (0.02–0.16)
Bio	18	< 0.01	***	0.39 (0.28–0.56)	< 0.01	**	0.47 (0.29–0.78)
VP	max	< 0.01	***	0.12 (0.07–0.21)	< 0.01	***	0.04 (0.02–0.08)
VP	min	0.01	*	1.84 (0.16–2.95)	< 0.01	***	3.07 (1.65–5.79)

P value 0=***, 0.001= **, 0.01= * 0.05=.

The AUC-ROC, sensitivity, and specificity are above 0.68 for both models (Table 7).

Table 7

Values of the Area under the curve (AUC), Specificity, and Sensitivity for the multivariate generalized linear models
Model	AUC-ROC	Specificity	Sensitivity
R. microplus	0.8360	0.6865	0.8547
A. cajennense s.l.	0.9009	0.7967	0.8957

Predictive model

Figure 5 shows the potential distribution of R. microplus obtained by Random Forest. The Amazon and Coastal zone are highly suitable, while only certain Andean areas were suitable habitats, particularly in zones of Andean valleys and foothills. Figure 6 is showing the suitability map for R. microplus with areas currently not agricultural masked out. For R. microplus the highest value of Percentage Increase in MSE (Regression) (%IncMSE) corresponds to Bio 14 followed by Bio 1 with 42.44 and 42.18 respectively (Fig. 7). Bio 4 and Bio 12 were also important variables. Thus, a combination of factors like temperature, humidity, and reduced change in seasonality were determinant factors for R. microplus presence.

Areas with high predicted suitability for A. cajennense s.l. are all found in the Coastal zone (Fig. 8). Predicted suitability is low in the Andean and Amazon zones. However,the foothills of the western cordillera also have a degree of predicted suitability. Figure 9 presents the suitability with non-agricultural areas masked out. With respect to the importance of the variables, the highest %incMSE values correspond to Bio14 and Bio4 with 36.74 and 33.71 respectively (Fig. 10). Probably, areas with marked seasonality, reduced isothermality are related to A. cajennese s.l. presence.

All provinces of the Coastal zone have a high suitability for both tick species, as well as Loja province in the Andean zone (Fig. 11).

All accuracy metrics for the Random Forest models were above 0.93 (Table 8).

Table 8

Accuracy, sensitivity, specificity, and kappa for the Random forest models for *R. microplus* y *A. cajennense* s.l..
Model	Accuracy	Sensitivity	Specificity	Kappa
R. microplus	0.968	0.970	0.966	0.933
A. cajennense s.l.	0.983	0.992	0.935	0.937

The models were also trained and tested with 80% and 20% of the entire data, respectively. The average of 10 random models is presented in Table 9.

Table 9

Average of accuracy metricts for 10 random cross validated models.
Model	Accuracy	Sensitivity	Specificity	Kappa
R. microplus	0.778	0.797	0.756	0.539
A. cajennense s.l.	0.878	0.925	0.634	0.569

The results of this study highlights the widespread distribution of R. microplus and A. cajennense s.l. ticks in tropical and subtropical areas of Ecuador, as supported by both scientific literature and the authors' own samplings (Escobar et al. 2015; Bustillos and Rodríguez 2016; Rodríguez-Hidalgo et al. 2017; Maya-Delgado et al. 2020; Chávez-Larrea et al. 2021; Guglielmone et al. 2021; Paucar et al. 2022). Earlier studies had identified isolated presence of R. microplus in the provinces of Los Ríos (Escobar et al. 2015), Pichincha, Tungurahua, Manabí (Diazalulema 2015; Rodríguez-Hidalgo et al. 2017; Chávez-Larrea et al. 2021), Napo, Sucumbíos, Orellana (Quezada and Quezada 2015; Insuaste Taipe 2021). A. cajennense s.l. was documented in El Oro (Nava et al. 2014), Pichincha and Manabí (Beati et al. 2013; Paucar et al. 2022). However, this information is scattered and needs to be assembled to get a consolidated understanding of the distribution of these species in continental Ecuador.

In this study, we showed the current distribution of R. microplus and A. cajennense s.l. in continental Ecuador and its association with bioclimatic variables was carried out in predictive models.

Climate conditions, particularly temperature, humidity, and precipitation, were found to be crucial factors influencing the distribution and development of ticks, which is in line with previous research (Pfäffle et al. 2013). For this study, bioclimatic variables like Bio1, Bio2, Bio3, Bio4, Bio12, Bio13, Bio14, Bio18, VP_min, and VP_max were highly significant in the models, with, with Bio1, Bio12, Bio14 and VPD variables having the highest (above 1) and lowest (below 1) odds ratio. This is coherent with Marques et al. (2020) who in their study determined that the bioclimatic variables from WorldClim, such as Bio1, Bio4, Bio12, Bio14, and relative humidity, are useful predictors for R. microplus. Likewise, Bio 18 contributed to the models developed by Namgyal et al. (2021) in addition to elevation and land cover, which were not evaluated in this study as we focused on climatic factors. These results are coherent with tick biology for both species studied (Estrada-Peña et al. 2006c, 2014; Pascoe et al. 2019). In the case of A. cajennense s.l., the study of Aguilar-Domínguez et al. (2021) with A. mixtum, species belonging to the cajennense complex, showed four important bioclimatic factors: Bio4, Bio6, Bio7 and Bio12, of which Bio4, and Bio12 are coherent with this study. A. cajennense s.l. has a suitable habitat in the coastal zone of Ecuador where rainy seasonality is marked but reduced changes in temperature exist. Thus, is thermality played an important role in.

At the farm level, factors such as the presence and abundance of hosts (cattle), as well as human prevention/control practices, such as acaricide management, cattle resistance to ticks, and organization of grazing systems, play a significant role in the distribution and abundance of cattle ticks (Estrada-Peña et al. 2005; Alemán Gaínza et al. 2014; Paucar-Quishpe et al. 2022). Those variables are difficult to mapping but its presence is important for tick presence. The study utilized a Random Forest model to predict suitable areas for R. microplus and A. cajennense s.l. based on bioclimatic factors. Random Forest was used because we have presence and absence data and following bibliographic recommendations. In another continent, Zannou et al. (2022) tested several models and found that Random Forest was an accurate model for habitat suitability of R. microplus.

Ecuador's diverse ecological formations associated with varied microclimates create an intricate landscape for tick distribution (Galeas et al. 2013). Our models suggest that R. microplus could potentially occur in most areas of Ecuador, except the Andes Mountains, with the Amazon and Coastal zones showing high suitability. However, caution is needed when considering areas that are currently unused for agricultural or livestock purposes (Figs. 6 and 9), like the Amazon zone, as they could become highly suitable if forest areas are cleared for pasture and cattle introduction (i.e. prospective scenario tested in this study). Additionally, the Amazon zone is known for its extensive forested regions and 16 Natural Protected Areas spanning 30,514 km2 (López. et al. 2012; Galeas et al. 2013) it is facing significant anthropic activities, such as oil extraction, mining, deforestation, road construction, colonization, and disorganized rural settlements, are transforming large forested habitats into fragmented landscapes, potentially leading to changes in tick distribution (López et al. 2012; Galeas et al. 2013; Alemán Gaínza et al. 2014; Cicuttin 2019; Vale et al. 2019; Galeas et al. 2013), in the Amazon zone abundant mammals, birds, reptiles, and amphibians live, which serve as hosts for other species of ticks such as Amblyomma latepunctatum Tonelli Rondelli, Amblyomma humerale Koch, Amblyomma dissimile Koch, etc. (Guglielmone et al. 2021). These new habitats experience changes in climate conditions, including increased temperatures and reduced humidity (Pfäffle et al. 2013). When humans enter these habitats with their domestic animals carrying cattle ticks, the ticks adapt easily to the new conditions and, lacking natural predators, establish large populations. This could result in the spread of tick-related problems to these suitable areas. Cattle mobility has been identified as a primary factor in the spread of cattle ticks (Chávez-Larrea et al. 2021), suggesting that tick-free areas may eventually face similar issues.

The study's findings diverge from some previous research. In our study, the Amazon zone and Coastal zone are highly suitable, as well as Andean valleys, and the eastern and western foothills of the Andes for R. microplus. This differs from results obtained at the regional level, for example, by Marques et al. (2020) who report high suitability in the Andean zone and medium suitability in the Coastal zone and the Amazon zone. Estrada-Peña (1999) showed Ecuador as non-suitable for R. microplus. In addition (Estrada-Peña et al. 2005) show only the northwestern part of the country (Esmeraldas and Carchi) as zones with high suitability in 1999, the northern provinces of the three regions, and parts of the southern zone as suitable zones in 2025 and 2050. This study provides a good estimation of the habitat suitability, with sensitivity of 0.97 and specificity of 0.96, similar values to Estrada-Peña (1999) with sensitivity of 0.91 and specificity of 0.88.

For A. cajennense s.l., the model in this study shows that the highly suitable areas are limited to the Coastal zone and areas near to the western foothills of the Andes. The model for Amblyomma mixtum Koch proposed by Nava et al. (2014) shows also highly suitability for the Coastal zone, similar to this study. Aguilar-Domínguez et al. (2021) describe the potential distribution of A. mixtum and show the Coastal zone of Ecuador as suitable as well, and for the coming 50 years. Our study also shows a very low suitability in certain areas of the Amazon where the presence of this species has not been reported and most of them correspond to non-livestock (agricultural) areas. A. mixtum species are known from western Ecuador (provinces of El Oro, Guayas, Los Ríos, Manabí and Pichincha) (Orozco Álvarez 2018; Paucar et al. 2022), where it is found in dry, semiarid, and riparian forests or savanna lowlands (Estrada-Peña et al. 2014). This tick only survives when the relative humidity in its microhabitat is not lower than 80% for extended periods of time (Pfäffle et al. 2013). A. cajennense s.l. spend more time and energy in finishing its life cycle because its way to feed (Polanco Echeverry and Ríos Osorio 2016). This factor, together with the fact that R. microplus has cattle as a specific host and that it can easily adapt to the tropical and subtropical habitats of Ecuador, has led to an abundance of more than 80% in the farms studied, while A. cajennense s.l. has an abundance of only about 15%.

Figure 11 highlights areas where both tick species are likely to coexist on the same farm. In a study conducted by Alonso-Díaz et al. (2013) in Mexico, all of the 43 farms surveyed had both species present. We found that 378 out of 2895 farms had both species, primarily in the Coastal zone and western foothills of the Andean Mountains. These tick species are closely associated with their respective hosts, with R. microplus primarily infesting cattle and A. cajennense s.l. primarily infesting equines, although the latter has also adapted well to cattle (Guglielmone et al. 2021).

This research is the first to provide a national-level assessment of tick distribution in continental Ecuadorian livestock farms, offering valuable insights into tick presence and absence across the country. This comprehensive understanding of tick distribution can aid in the development of effective tick control and management plans, considering the differences in tick species, their distribution, and their biological characteristics.

The study emphasizes the importance of continuous research to monitor tick populations as tick distribution may change over time due to various factors, including climate change and human activities. Moreover, knowing the main tick species and their spatial distribution is crucial for developing targeted strategies to mitigate tick-related problems and protect livestock health and productivity. The study's findings call for the implementation of tick prevention/control plans considering the specific ecological contexts and host interactions, which could help reduce the negative impacts of ticks on livestock farming. Moreover, it is crucial to consider diverse approaches for their prevention/control due to the broad host range of A. cajennese s.l., and the specificity of R. microplus for cattle. Additionally, it highlights the importance of addressing the challenges posed by R. microplus resistance to acaricides, which has become a significant concern for livestock farmers in Ecuador (Rodríguez-Hidalgo et al. 2017; Maya-Delgado et al. 2020; Paucar-Quishpe et al., 2023).

In conclusion, this study significantly contributes to the understanding of tick distribution in Ecuador, shedding light on the factors influencing their presence and abundance. The results offer valuable insights for policymakers, farmers, and researchers to develop effective tick preventive/control plans and protect livestock health in the region. However, continuous research and monitoring are necessary to keep abreast of the evolving tick distribution patterns and make informed decisions for tick management and livestock production in the future.

AUTHORSHIP CLARIFIED

All authors whose names appear on the submission

1) XP, SV, LR, RR, SE, made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data;

2) XP, SV, LR, RR, SE, CS drafted the work or revised it critically for important intellectual content;

3) XP, SV, LR, RR, SE, FV, WB, CS, MC approved the version to be published; and

4) XP, SV, LR, RR, SE, FV, WB, CS, MC agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

FUNDING

Sample collection for this study was supported by Belgian Development Cooperation (DGD), Ministry of Agriculture and Livestock of Ecuador (MAG), and Universidad Central del Ecuador (UCE). The project was approved and received national consent by the Ministry of Agriculture and Livestock of Ecuador (MAG) (N◦ SENPLADES-SGPBV-2012-0237- OF—Programa Nacional de Cárnicos). The PhD program (XP) was funded by the Academy of Research and Higher Education (ARES) through the Research for Development Project (PRD) B1.31203.006-E6 entitled ‘Socio-eco-epidemiology of ticks, tick-borne parasites, acaricide resistance and residual effects of acaricides in tropical Ecuadorian livestock: environmental, animal and public health impacts’, which involves universities from Ecuador (CIZ, Universidad Central del Ecuador) and Belgium (UCLouvain and ULiège).

INFORMED CONSENT STATEMENT

The participant farmers were properly informed and gave their consent prior to collect ticks over their animals.

DATA AVAILABILITY STATEMENT

The data that support the results of this study are available from the corresponding author upon request.

ACKNOWLEDGMENTS

Our thanks to the Belgian Development Cooperation (DGD), Ministry of Agriculture and Livestock of Ecuador (MAG), Universidad Central del Ecuador (UCE), and Instituto de Investigación en Zoonosis (CIZ), for funding this research. To the Academy of Research and Higher Education (ARES) for funding the PhD program and UCLouvain for hosting the PhD program. In addition, we thank all participating farmers, local veterinarians, and MAG technicians who supported the field collections.

CONFLICTS OF INTEREST

The authors declare no conflict of interest.

Aguilar-Domínguez M, Moo-Llanes DA, Sánchez-Montes S et al (2021) Potential distribution of Amblyomma mixtum (Koch, 1844) in climate change scenarios in the Americas. Ticks Tick Borne Dis 12. https://doi.org/10.1016/j.ttbdis.2021.101812
Alcala-Canto Y, Figueroa-Castillo JA, Ibarra-Velarde F et al (2018) Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution. Geospat Health 13:110–117. https://doi.org/10.4081/gh.2018.624
Alemán Gaínza Y, Martínez Marrero S, Corona González B (2014) Ticks of veterinary interest in Cuba, and its importance in the changing climatic conditions. Rev Electron Vet 15
Alonso-Díaz MA, Fernández-Salas A, Martínez-Ibáñez F, Osorio-Miranda J (2013) Amblyomma cajennense (Acari: Ixodidae) tick populations susceptible or resistant to acaricides in the Mexican Tropics. Vet Parasitol 197:326–331. https://doi.org/10.1016/j.vetpar.2013.06.004
Beati L, Nava S, Burkman EJ et al (2013) Amblyomma cajennense (Fabricius, 1787) (Acari: Ixodidae), the Cayenne tick: Phylogeography and evidence for allopatric speciation. BMC Evol Biol 13. https://doi.org/10.1186/1471-2148-13-267
Bianchi MW, Barré N, Messad S (2003) Factors related to cattle infestation level and resistance to acaricides in Boophilus microplus tick populations in New Caledonia. Vet Parasitol 112:75–89. https://doi.org/10.1016/S0304-4017(02)00415-6
Bustillos R, Rodríguez R (2016) Ecologia parasitaria de Riphicephalus microplus en bovinos, 1st edn. Editorial académica española
Chávez-Larrea MA, Cholota-Iza C, Medina-Naranjo V et al (2021) Detection of Babesia spp. In high altitude cattle in ecuador, possible evidence of the adaptation of vectors and diseases to new climatic conditions. Pathogens 10. https://doi.org/10.3390/pathogens10121593
Cicuttin GL (2019) Estudio epidemiológico de las garrapatas presentes en un área urbana protegida en la Ciudad Autónoma de Buenos Aires. Universidad Nacional del Litoral
De Clercq EM, Leta S, Estrada-Peña A et al (2015) Species distribution modelling for Rhipicephalus microplus (Acari: Ixodidae) in Benin, West Africa: Comparing datasets and modelling algorithms. Prev Vet Med 118:8–21. https://doi.org/10.1016/j.prevetmed.2014.10.015
Diazalulema S (2015) Identificación Taxonómica De Garrapatas En Ganado Bovino De La Parroquia La Matriz Del Cantón Patate. Universidad Técnica de Ambato
Dzemo WD, Thekisoe O, Vudriko P (2022) Development of acaricide resistance in tick populations of cattle: A systematic review and meta-analysis. Heliyon 8:e08718. https://doi.org/10.1016/j.heliyon.2022.e08718
Escobar A, Cevallos O, Villarreal P et al (2015) Prevalence and detection by nested PCR of Anaplasma marginale in cattle and tick in the center of the coast of Ecuador. Cienc y Tecnol 2015 8:11–17
Estrada-Peña A (1999) Geostatistics and remote sensing using NOAA-AVHRR satellite imagery as predictive tools in tick distribution and habitat suitability estimations for Boophilus microplus (Acari: Ixodidae) in South America. Vet Parasitol 81:73–82. https://doi.org/10.1016/S0304-4017(98)00238-6
Estrada-Peña A (2023) The climate niche of the invasive tick species Hyalomma marginatum and Hyalomma rufipes (Ixodidae) with recommendations for modeling exercises. Exp Appl Acarol 231–250. https://doi.org/10.1007/s10493-023-00778-3
Estrada-Peña A, Bouattour A, Camicas JL et al (2006a) The known distribution and ecological preferences of the tick subgenus Boophilus (Acari: Ixodidae) in Africa and Latin America. Exp Appl Acarol 38:219–235. https://doi.org/10.1007/s10493-006-0003-5
Estrada-Peña A, Corson M, Venzal JM et al (2006b) Changes in climate and habitat suitability for the cattle tick Boophilus microplus in its southern Neotropical distribution range. J Vector Ecol 31:158–167. https://doi.org/10.1645/0022-3395(2001)087[0978:cwacih]2.0.co;2
Estrada-Peña A, García Z, Sánchez HF (2006c) The distribution and ecological preferences of Boophilus microplus (Acari: Ixodidae) in Mexico. Exp Appl Acarol 38:307–316. https://doi.org/10.1007/s10493-006-7251-2
Estrada-Peña A, Sánchez Acedo C, Quílez J, Del Cacho E (2005) A retrospective study of climatic suitability for the tick Rhipicephalus (Boophilus) microplus in the Americas. Glob Ecol Biogeogr 14:565–573. https://doi.org/10.1111/j.1466-822X.2005.00185.x
Estrada-Peña A, Tarragona EL, Vesco U et al (2014) Divergent environmental preferences and areas of sympatry of tick species in the Amblyomma cajennense complex (Ixodidae). Int J Parasitol 44:1081–1089. https://doi.org/10.1016/j.ijpara.2014.08.007
Forero-Becerra E, Acosta A, Benavides E et al (2022) Amblyomma mixtum free-living stages: Inferences on dry and wet seasons use, preference, and niche width in an agroecosystem. Yopal, Casanare, Colombia)
Galeas R, Guevara JE, Medina-Torres B et al (2013) Sistema de clasificación de los ecosistemas del Ecuador continental. Quito, Ecuador
González-Acuña D, Guglielmone AA (2005) Ticks (Acari: Ixodoidea: Argasidae, Ixodidae) of Chile. Exp Appl Acarol 35:147–163. https://doi.org/10.1007/s10493-004-1988-2
Guerrero R (1996) Las garrapatas de Venezuela (acarina: ixodoidea): listado de especies y claves para su identificación / Ticks of Venezuela (acarina: ixodoidea): list of specific and clavichord for your classification. Bol Dir Malariol Saneam Ambient 36(1/2):1–24
Guglielmone AA, Nava S, Robbins RG (2021) Neotropical Hard Ticks (Acari: Ixodida: Ixodidae). Springer International Publishing, Cham, Switzerland
Guillén NX, Muñoz LE (2013) Estudio taxonómico a nivel de género de garrapatas en ganado bovino de la parroquia Alluriquín - Santo Domingo de los Tsáchilas. Universidad de las Fuerzas Armadas (ESPE)
Insuaste Taipe EP (2021) Identificación molecular de Babesia bovis y B. bigemina en garrapatas. Riphicephalus (Boophilus) microplus de la región norte amazonica del Ecuador. Universidad Internacional (SEK)
Karger DN, Conrad O, Böhner J et al (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4:1–20. https://doi.org/10.1038/sdata.2017.122
Kasaija PD, Estrada-Peña A, Contreras M et al (2021) Cattle ticks and tick-borne diseases: a review of Uganda’s situation. Ticks Tick Borne Dis 12. https://doi.org/10.1016/j.ttbdis.2021.101756
Kopsco HL, Smith RL, Halsey SJ (2022) A Scoping Review of Species Distribution Modeling Methods for Tick Vectors. Front Ecol Evol 10. https://doi.org/10.3389/fevo.2022.893016
Lima WS, Ribeiro MF, Guimaraes MP (2000) Seasonal variation of Boophilus microplus (Canestrini, 1887) (Acari: Ixodidae) in cattle in Minas Gerais state, Brazil. Trop Anim Health Prod 32:375–380. https://doi.org/10.1023/A:1005229602422
López AV, Espíndola F, Calles LJ (2012) Amazonía ecuatoriana bajo presión. Quito, Ecuador
Marques R, Krüger RF, Peterson AT et al (2020) Climate change implications for the distribution of the babesiosis and anaplasmosis tick vector, Rhipicephalus (Boophilus) microplus. Vet Res 51:1–10. https://doi.org/10.1186/s13567-020-00802-z
Maya-Delgado A, Madder M, Benítez-Ortíz W et al (2020) Molecular screening of cattle ticks, tick-borne pathogens and amitraz resistance in ticks of Santo Domingo de los Tsáchilas province in Ecuador. Ticks Tick Borne Dis 11:101492. https://doi.org/10.1016/j.ttbdis.2020.101492
Ministerio de Agricultura y Ganadería (MAG) (2014) 2013–2014 - Mapa de Cobertura y Uso de la tierra en el Ecuador continental - Categoría: Tierra en transición, escala 1:100.000, año 2013–2014. http://geoportal.agricultura.gob.ec/geonetwork/srv/spa/catalog.search#/metadata/35ccb71a-c0a9-485a-84d4-965da38410f8
Namgyal J, Lysyk TJ, Couloigner I et al (2021) Identification, distribution, and habitat suitability models of ixodid tick species in cattle in eastern Bhutan. Trop Med Infect Dis 6. https://doi.org/10.3390/tropicalmed6010027
Nava S, Beati L, Labruna MB et al (2014) Reassessment of the taxonomic status of Amblyomma cajennense (Fabricius, 1787) with the description of three new species, Amblyomma tonelliae n. sp., Amblyomma interandinum n. sp. and Amblyomma patinoi n. sp., and reinstatement of Amblyomma mixtum Koch, 1. Ticks Tick Borne Dis 5:252–276. https://doi.org/10.1016/j.ttbdis.2013.11.004
Nava S, Mastropaolo M, Guglielmone AA, Mangold AJ (2013) Effect of deforestation and introduction of exotic grasses as livestock forage on the population dynamics of the cattle tick Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) in northern Argentina. Res Vet Sci 95:1046–1054. https://doi.org/10.1016/j.rvsc.2013.09.013
Orozco Álvarez GE (2018) Distribución espacial de garrapatas que afectan a las ganaderías ecuatorianas de las tres regiones, usando como referencia la línea equinoccial. Universidad Central del Ecuador
Pascoe EL, Marcantonio M, Caminade C, Foley JE (2019) Modeling potential habitat for Amblyomma tick species in California. Insects 10:1–19. https://doi.org/10.3390/insects10070201
Paucar-Quishpe V, Pérez-Otáñez X, Rodríguez-Hidalgo R et al (2023) An economic evaluation of cattle tick acaricide-resistances and the financial losses in subtropical dairy farms of Ecuador: A farm system approach. PLoS ONE 18:1–31. https://doi.org/10.1371/journal.pone.0287104
Paucar V, Pérez-Otáñez X, Rodríguez-Hidalgo R et al (2022) The Associated Decision and Management Factors on Cattle Tick Level of Infestation in Two Tropical Areas of Ecuador. Pathogens 11:1–20. https://doi.org/10.3390/pathogens11040403
Paucar V, Ron-Román J, Benítez-Ortiz W et al (2021) Bayesian estimation of the prevalence and test characteristics (Sensitivity and specificity) of two serological tests (rb and sat-edta) for the diagnosis of bovine brucellosis in small and medium cattle holders in ecuador. Microorganisms 9:1–20. https://doi.org/10.3390/microorganisms9091815
Pfäffle M, Littwin N, Muders SV, Petney TN (2013) The ecology of tick-borne diseases. Int J Parasitol 43:1059–1077. https://doi.org/10.1016/j.ijpara.2013.06.009
Polanco Echeverry DN, Ríos Osorio LA (2016) Biological and ecological aspects of hard ticks,Aspectos biológicos y ecológicos de las garrapatas duras, Aspectos biológicos e ecológicos dos carrapatos duros. 17:81–95
Pothmann D, Poppert S, Rakotozandrindrainy R et al (2016) Prevalence and genetic characterization of Anaplasma marginale in zebu cattle (Bos indicus) and their ticks (Amblyomma variegatum, Rhipicephalus microplus) from Madagascar. Ticks Tick Borne Dis 7:1116–1123. https://doi.org/10.1016/j.ttbdis.2016.08.013
Primicias (2022) La inversión en el sector agrícola representa una apuesta para el futuro Para hacer uso de este contenido cite la fuente y haga un enlace a la nota original en Primicias.ec: https://www.primicias.ec/noticias/patrocinado/la-inversion-en-el-sector-agricola-. https://www.primicias.ec/noticias/patrocinado/la-inversion-en-el-sector-agricola-representa-una-apuesta-para-el-futuro/
Quezada L, Quezada N (2015) Efectividad de Beauveria spp. como controlador biológico de garrapatas Rhipicephalus spp. en ganado bovino en la provincia de Orellana. Escuela superior politécnica de Chimborazo sede Orellana
Rodríguez-Hidalgo R, Pérez-Otáñez X, Garcés-Carrera S et al (2017) The current status of resistance to alpha-cypermethrin, ivermectin, and amitraz of the cattle tick (Rhipicephalus microplus) in Ecuador. PLoS ONE 12:1–15. https://doi.org/10.1371/journal.pone.0174652
Vale P, Gibbs H, Vale R et al (2019) The Expansion of Intensive Beef Farming to the Brazilian Amazon. Glob Environ Chang 57:101922. https://doi.org/10.1016/j.gloenvcha.2019.05.006
Voltzit VO (2007) A review of Neotropical Amblyomma species (Acari: Ixodidae). Acarina 15:3–134
Wollaeger H, Runkle E (2015) VPD vs Relative Humidity. Vapor- pressure deficit is independent of temperature and is a more accurate measure to predict plant transpiration and water loss than relative humidity. Insid Grow 28–29
Zannou OM, Da Re D, Ouedraogo AS et al (2022) Modelling habitat suitability of the invasive tick Rhipicephalus microplus in West Africa. Transbound Emerg Dis 69:2938–2951. https://doi.org/10.1111/tbed.14449

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High-resolution prediction models for Rhipicephalus microplus and Amblyomma cajennense s.l. ticks affecting cattle and their spatial distribution in continental Ecuador using bioclimatic factors

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