Mapping heartwater risk in Guadeloupe: a combination of spatial modelling approaches

Mapping heartwater risk in Guadeloupe: a combination of spatial modelling approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mapping heartwater risk in Guadeloupe: a combination of spatial modelling approaches Victor Dufleit, Eric Etter, Laure Guerrini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7142010/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Heartwater is a tick-borne disease affecting livestock in Africa and the Caribbean, including Guadeloupe, where it threatens animal health and productivity. While Amblyomma variegatum has long been recognized as the primary vector, recent studies suggest Rhipicephalus microplus may also transmit Ehrlichia ruminantium , the causative agent. This study presents a spatial modelling framework to assess heartwater risk across Guadeloupe. Tick presence data collected during livestock inspections were combined with environmental variables derived from satellite imagery and other geospatial sources. Ecological Niche Factor Analysis identified key environmental predictors, which were then used to build MaxEnt models and generate suitability maps for both tick species. These maps revealed distinct ecological preferences and were integrated with cattle density data using a Multi-Criteria Decision Analysis approach, with expert-derived weighting, to produce a composite risk index. The resulting maps provide the first spatially explicit assessment of heartwater risk in Guadeloupe. This approach offers a reproducible method for mapping tick-borne disease risk in data-limited tropical regions and can guide targeted surveillance and control strategies. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Heartwater is an acute, often fatal tick-borne disease of ruminants caused by Ehrlichia ruminantium 1 , with significant economic impacts in affected regions 2 – 4 . In sub-Saharan Africa and the Caribbean, ticks of the genus Amblyomma are recognized as the primary vectors of the disease 5 . In Guadeloupe, the pathogen was first detected in 1980 6 , likely introduced via cattle imported from West Africa infested with Amblyomma variegatum 7 . To date, A. variegatum remains the only confirmed vector in the region. However, recent studies from West Africa 8 , 9 suggest that Rhipicephalus microplus , another widespread cattle tick also present in Guadeloupe, may play a role in E. ruminantium transmission. Already established as a vector of anaplasmosis and babesiosis in Guadeloupe 10 , the presence of both tick species represents a substantial challenge for livestock health, especially in a context of declining production 11 , 12 . Ecological niche modelling has proven useful in identifying the potential distribution of vector species using environmental predictors 13 , 14 . Among the available methods, maximum entropy modelling (MaxEnt) is widely used for its ability to work with presence-only data, an advantage for species such as A. variegatum , which are difficult to detect or sample consistently using traps 15 , 16 . MaxEnt has been successfully applied to model the potential distribution of A. variegatum in the Americas 17 , and R. microplus on both global and regional scales 18 – 20 . In parallel, spatial risk assessment methods such as Multi-Criteria Decision Analysis (MCDA) have been used to map vector-borne disease risk by integrating environmental and epidemiological data layers 21 . While various methods are available for diseases risk mapping 22 , MCDA enables the integration and weighting of diverse predictor layers, such as host density, climate, and vegetation, based on expert knowledge or literature-derived evidence 23 . This method has shown particular relevance for diseases whose transmission is influenced by multiple interacting environmental factors 24 – 26 . Despite existing data on tick infestations and E. ruminantium circulation 27 – 30 , no comprehensive spatial modelling study has yet been conducted in Guadeloupe. This study aims to fill that gap by introducing an integrative framework for spatial heartwater risk mapping. We combine field-collected presence data for A. variegatum and R. microplus , serological data, environmental predictors derived from remote sensing, and disaggregated cattle density data 31 . Key predictors were first identified using Ecological Niche Factor Analysis (ENFA) 32 , 33 , and then integrated into MaxEnt models. The resulting tick suitability maps were integrated into an MCDA to produce the first spatially explicit heartwater risk maps for the Guadeloupe archipelago. Material and method Study area Guadeloupe is an archipelago located in the Lesser Antilles in the Caribbean and constitutes an overseas region of France. It comprises several islands, five of which are inhabited. The two largest are Basse-Terre to the west and Grande-Terre to the east. The archipelago also includes three administrative dependencies: La Désirade (northeast of Grande-Terre), Marie-Galante (south of Grande-Terre), and Les Saintes (southeast of Basse-Terre). The region is characterized by a tropical climate with notable spatial variation. Basse-Terre, which hosts the Soufrière volcano and the island’s highest elevations, experiences significantly higher annual rainfall compared to the rest of the archipelago. This disparity is largely due to orographic effects, as the mountainous terrain intercepts the easterly trade winds and moisture-laden clouds, resulting in a much wetter climate, particularly in southern Basse-Terre 34 . Figure 1 provides a geographical overview of Guadeloupe, with predicted cattle density shown as a background layer. These density estimates, extracted from a prior study based on a census disaggregation methodology 31 , were a key input for the risk mapping analyses conducted in this study. Data Vector presence and heartwater serological evidence Tick occurrence data were collected for the two main tick species infesting ruminants in Guadeloupe: Amblyomma variegatum and Rhipicephalus microplus . Ticks were obtained through physical examination of cattle, sheep, and goats, following the standardized protocol described by Stachurski (2000) 35 . Simultaneously, blood samples were collected during animal handling for serological testing of E. ruminantium antibodies using the MAP1B ELISA assay. Further details on the sampling strategy and laboratory protocols are available in 36 . For spatial modelling, tick presence data, defined as the locations of infested animals, were projected onto a 225 m resolution raster grid 37 , consistent with previous cattle density mapping in the region 31 . To validate the MCDA-based risk assessment, a binary raster layer representing the presence or absence of E. ruminantium exposure was also produced from the serological dataset. Initial compilation of predictors To support ecological niche modelling of Amblyomma variegatum and Rhipicephalus microplus in Guadeloupe, 32 environmental raster layers were compiled to characterize habitat conditions relevant to tick ecology 20 . Topographic layers: Elevation data were provided by BRGM Guadeloupe (Bureau de Recherche Géologiques et Minières), from which a slope raster layer was derived using QGIS. Climatic layers: Long-term precipitation data (1990–2022) were retrieved from Météo-France (2025). Median annual precipitation values were calculated and interpolated into a continuous raster surface via ordinary kriging (via the gstat R package 40 , 41 ), resulting in an annual precipitation raster. Land cover layers: High-resolution land cover information was extracted from the Karucover 2022 database 42 , using the fourth-level classification. The following adjustments were applied: - Polygons classified as “Built”, “Non-built”, “Mineral materials zone,” and “Composite material zone” were merged into a single “Artificial areas “category. - “Stone”, “Rock”, and “Sand/silt” classes were grouped under “Stone/sediment” category. Proportions of each land cover class were calculated within raster pixels to produce nine continuous raster layers representing land cover predictors. Remote sensing layers: Environmental variables derived from MODIS products were processed using the MODIStsp R package 43 . The selected datasets included: - Land surface temperature (LST) for both day and night at 1 km resolution 44 . - Surface reflectance bands at 500 m resolution: Band 1 (Red), Band 2 (Near Infrared), and Band 6 (Short-Wave Infrared) 45 . LST data were filtered using quality assurance flags ("QAday" and "QAnight"), while surface reflectance data were masked using "state_cloud" and "state_int_cld" filters. A land mask based on the departmental boundary of Guadeloupe was applied to all remote sensing products. Two vegetation indices were derived from the surface reflectance data: - Normalized Difference Vegetation Index (NDVI): a proxy for vegetation greenness and productivity 46 . - Normalized Difference Moisture Index (NDMI): an indicator of surface moisture 47 . Time series for four key indicators (LST_Day, LST_Night, NDVI, NDMI) from 2019 to 2024 were processed using Fourier analysis, following the method of Scharlemann et al., (2008) 48 . For each variable, statistical summaries—including mean, minimum, maximum, and amplitudes of the first and second harmonics—were extracted for inclusion in the models. All raster layers were resampled to a common spatial resolution of 225 m using bilinear interpolation 49 , to ensure spatial consistency with cattle density data and modelling framework. To address data availability constraints in other Caribbean regions, two predictor sets were prepared: one including the Karucover-derived land cover layers, and another excluding them. This dual approach enabled an evaluation of the added predictive value of high-resolution land cover data in improving model performance. Predictor selection via Ecological Niche Factor Analysis (ENFA) To guide predictor selection and understand species-environment relationships, Ecological Niche Factor Analysis (ENFA) was conducted using the adehabitatHS R package 50 . ENFA is a presence-only multivariate method that compares environmental conditions at species presence locations with the background environmental space of the study area 33 . Two key metrics were extracted: Marginality: quantifies the degree to which the environmental conditions at species presence locations deviate from the average conditions across the study area. A high marginality value indicates that the species occurs in environments that differ significantly from the general landscape 32 . Specialization: reflects the degree to which a species is restricted to a narrow range of environmental conditions compared to the total available environmental space. Higher values indicate a more constrained ecological niche. ENFA results were visualized using factor maps, aiding both in the interpretation of ecological patterns and in guiding predictor selection. Predictors with significantly high absolute marginality values were retained for subsequent modelling steps, as these likely represent key ecological determinants of tick occurrence 51 . To control for multicollinearity among the retained predictors, Variance Inflation Factor (VIF) analysis was applied iteratively 52 . Predictors with VIF values greater than 10 were considered highly collinear; in such cases, ecologically meaningful variables were retained based on the specialization metric and prior literature and expert knowledge. The process was repeated until all included predictors had VIF values below the threshold of 10 53 , ensuring a parsimonious yet ecologically informative set of covariates for modeling. Niche prediction using MaxEnt The Maximum Entropy (MaxEnt) modeling framework was selected due to the nature of the species occurrence data, which consisted exclusively of presence-only records 54 , 55 . This approach is particularly appropriate in contexts where absence data are unreliable or unavailable, such as in the case of Amblyomma variegatum , for which no effective trapping technique currently exists 16 . Instead, presence data were obtained via direct inspection of ruminants during field surveys, with GPS coordinates recorded for each infested host. This method, previously recommended in Guadeloupe 15 , may not reliably capture tick absence, as negative information may result from transient factors like recent acaricide treatments rather than true environmental exclusion. A central feature of MaxEnt is its use of background points (pseudo-absence), which represent the range of environmental conditions across the study area where the species was not recorded 56 . The algorithm estimates the potential ecological niche by identifying the probability distribution of environmental conditions that maximizes entropy, that is, the most uniform distribution possible, subject to constraints imposed by the environmental characteristics at known presence locations. Model tuning was conducted using the ENMeval R package 57 with focus on optimizing two key parameters: the regularization multiplier (RM) and feature classes (FC). The RM, also referred to as the β-parameter, governs the model complexity by penalizing overfitting, higher values result in smoother, more generalized predictions. RM values ranging from 1 to 5 were tested. Feature classes specify the allowable relationships between predictors and species occurrence, and combinations of linear, quadratic, hinge, and product features were evaluated. “Linear only” models were excluded to avoid oversimplification, and “threshold” feature were omitted to prevent abrupt spatial changes in suitability predictions. The optimal model configuration was selected based on the lowest corrected Akaike Information Criterion (AICc) 57 value among all candidate models. MaxEnt models were developed for each tick species using the occurrence records collected during field surveys: Amblyomma variegatum : 196 presence records aggregated into 105 unique raster pixels. Rhipicephalus microplus : 132 presence records aggregated into 74 unique pixels. Environmental predictors previously selected through ENFA were included in the modelling. For both species, 10,000 background points were randomly generated. The “block” method of spatial partitioning was applied to separate training and testing subsets for cross-validation. Model fitting was performed using the ‘maxent.jar’ implementation as described by Phillips et al. (2017) 58 . From the final best-fitting models, variable importance metrics and response curves were extracted to assess the influence of individual predictors on species distribution. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve, and the Area Under the Curve (AUC) statistic, providing a quantitative measure of discriminatory power 59 . Spatial multicriteria decision analysis to estimate Ehrlichia ruminantium risk To assess the spatial risk of Ehrlichia ruminantium infection, composite risk maps were developed by integrating the environmental suitability maps for the two tick vectors Amblyomma variegatum and Rhipicephalus microplus with a host density raster layer, using a Multi-Criteria Decision Analysis (MCDA) approach 60 . Although the role of R. microplus in the transmission of E. ruminantium has only been confirmed under experimental conditions 9 , this vector was included in the model to reflects its ecological presence and potential epidemiological relevance. The MCDA process incorporated a weighting mechanism to modulate the contribution of R. microplus relative to A. variegatum . The livestock density map (Fig. 1 ) was produced through spatial disaggregation of municipal-level census data following the Gridded Livestock of the World (GLW) methodology 61 , as modified for Guadeloupe by 31 . While previous studies have proposed risk factors for E.ruminantium infection 62 – 64 , many are constrained by limited spatial resolution or regional specificity. Therefore, the present risk model was structured around three spatially explicit layers, reflecting vectors and host dynamics: livestock density and environmental suitability for both A. variegatum and R. microplus 65 . In line with standard MCDA procedures, each input raster was transformed using a function reflecting hypothesized relationships with disease risk. Due to limited quantitative data describing the direct association between E. ruminantium transmission and specific environmental or host-related factors, an increasing linear transformation was applied to all three layers, under the assumption that risk increases proportionally with both host density and tick vector suitability. To assign relative weights to the three risk factors, a structured expert elicitation was conducted, following the guidelines outlined by Greene et al., (2011) 66 . Subject-matter expert were asked to score each factor on a scale from 1 to 10, reflecting its perceived importance in E. ruminantium transmission dynamics. These scores were then aggregated and normalized to a 0 − 1 scale, ensuring that the sum of all weights equaled 1 67 . Final risk scores were calculated using a weighted linear combination of the three transformed raster layers 26 . Model validation was performed using ROC curve analysis and AUC metrics, based on presence/absence data from serological testing 68 . Each pixel was assigned a value of 1 if serological evidence of E. ruminantium exposure was found in the corresponding sampled location, and 0 otherwise. Following recommendations from the WOAH terrestrial manual statement 69 , MAP1B test should only be considered at herd level to describe presence or absence of E.ruminantium . A pixel was considered seronegative only if at least three animals sampled within that pixel tested negative. Pixels that did not meet this threshold were excluded from the validation dataset to reduce false negatives. To assess model robustness, a "one-at-a-time" (OAT) sensitivity analysis was performed 70 . In this approach, the weight of each input factor was incrementally varied by ± 0.2 across 40 simulation steps, while proportionally adjusting the remaining weights to maintain a total weight sum of 1. For each iteration, the average absolute rate of change in the predicted risk values was computed. An uncertainty map was subsequently produced, defined as the standard deviation of the predicted values across all sensitivity scenarios. All spatial MCDA procedures were implemented using the spatMCDA R package 70 , and model evaluation metrics (ROC and AUC) were computed using the pROC package 71 . Ethical approval and compliance Tick presence and cattle serological data were collected as part of survey conducted under the RACE and TISARU projects. The study protocol was reviewed and approved by the relevant ethical authority, under APAFIS approval number #43250-2023050210511531 v3, prior to implementation. All methods were carried out in accordance with relevant guidelines and regulations. Results Tick presence and Erlichia ruminantium serological evidence All inhabited islands of the Guadeloupe archipelago were surveyed during the field campaign. Blood sampling confirmed the presence of Ehrlichia ruminantium infection across all islands 36 . A total of 421 animals were sampled, including 261 cattle, 135 goats, and 25 sheep. Serological analysis using the MAP1-B ELISA assay identified 103 animals as positive for E. ruminantium antibodies, including 76 cattle and 27 goats 36 . Regarding tick infestation, 189 animals (44%) were found to be infested with Amblyomma variegatum , 130 animals (30%) with Rhipicephalus microplus , and 92 animals (22%) were co-infested with both tick species. Among the 227 animals infested with at least one tick species (54% of the total sample), co-infestation represented approximately 40% of cases. No ticks were detected on inspected animals in La Désirade, and Rhipicephalus microplus was absent from the islands of Les Saintes. After spatial projection onto a 225-meter resolution grid, the 189 and 130 tick presence records were aggregated into 105 and 74 presence pixels for A. variegatum and R. microplus , respectively. Of these, 64 pixels indicated co-occurrence of both tick species. For E. ruminantium presence/absence data used in MCDA model validation, animal sampling data were summarized across 157 pixels locations. Among these, 64 pixels exhibited seropositive results in at least one animal. The remaining pixels were characterized by at least three animals with negative test results. Vectors’ Ecological niches ENFA Ecological niche factor analysis realised for both A. variegatum and R. microplus revealed broadly similar environmental preferences (Fig. 2 ). Temperature-related variables, including mean, minimum, and maximum values, as well as day and night land surface temperatures (LSTD, LSTN), along with the first harmonic amplitude (a1_LSTN), NDVI, NDMI, and amplitude of second harmonics of NDMI (a2_NDMI) displayed high positive marginality in both species. Specifically, A. variegatum showed a particularly strong association with a1_LSTD, a pattern not observed for R. microplus . In contrast, both species exhibited strong negative marginality for precipitation, elevation, slope and NDMI-related variables (mean, min and max), which consistently surpassed their NDVI counterparts. A distinguishing feature of R. microplus was a strong negative marginality for the second harmonic amplitude of night temperature (a2_LSTN), absent in A. variegatum . Additionally, high-resolution land cover variables, especially the proportions of “Grass” and “Forest” classes, exerted marked influence on niche definition for both species. MaxEnt modelling results Based on the ENFA results, an initial set of 18 environmental predictors was selected for ecological niche modelling of A. variegatum and R. microplus . These included: - Statistical summaries: mean, minimum, and maximum values of LSTD, LSTN, NDMI and NDVI;- Fourier components: first harmonic amplitudes of NDMI, NDVI and LSTN, and the second harmonic amplitude of NDMI; - Topographic variables: elevation and slope; - Precipitation: annual cumulative precipitation; - Species-specific variables: the first harmonic amplitude of LSTD for A. variegatum; the second harmonic amplitude of LSTN for R. microplus . In models including land cover data, the predictors of “Forest” and “Grass” cover classes were added, based on their high marginality values identified via ENFA. Analysis of VIF exhibited significant multicollinearity among several predictors (VIF > 10). Consequently, mean values of LSTD, LSTN, NDMI, and NDVI were removed, as minimum and maximum values were considered more relevant for capturing ecological constraints. Additionally, maximum LSTN and first harmonic amplitude of NDMI were excluded due to persistent collinearity with elevation. After variable reduction, 12 predictors were retained in the baseline models, and 14 predictors in the models incorporating land cover data. Model tuning and performance Optimal MaxEnt models for A. variegatum used a regularization multiplier (β) of 2 (without land cover) and 3 (with land cover), employing only linear and quadratic functions to represent the effects of predictors on suitability. For R. microplus , the best models were obtained with β = 5, both with and without landcover data. In the model without land cover, linear, quadratic, hinge and product feature classes were selected. In the model with land cover data, the product feature class was not included in the optimal configuration. Response curves for the predictors included in each model are provided in supplementary materials SM1-4. Model predictions Predicted habitat suitability maps from the best-fitted models are displayed in Fig. 3 . The spatial predictions of the two tick species presented a Pearson correlation coefficient of 0.86 (without land cover) and 0.93 (with landcover), both statistically significant (p-value < 0.05). Despite the high correlation observed between the ecological niches of Amblyomma variegatum and Rhipicephalus microplus , the predicted spatial distribution patterns revealed notable differences. Both species showed highest environmental suitability in Marie-Galante. However, A. variegatum , also exhibited similarly high suitability values across the plains of Grande-Terre, whereas R. microplus showed broadly high—but not peak—suitability in the same region. On Basse-Terre, A. variegatum had medium to high suitability in the northeast zones and isolated pockets in the southwest. R. microplus displayed a comparable distribution but slightly higher suitability in the southeastern part of the island. For both species, the central mountainous area of Basse-Terre consistently showed low environmental suitability. In Les Saintes, A. variegatum was associated with low to medium suitability, while R. microplus exhibited medium to high suitability values. The inclusion of land cover data as a predictor significantly influenced model outputs by constraining high suitability areas to grassland zones. Nonetheless, the patterns observed in the models without land cover data remained consistent with the revised predictions. With land cover included, the highest suitability values (> 0.9) for R. microplus were predicted in both Marie-Galante and Grande-Terre. Such a modifications was not observed for A. variegatum . Permutation importance analysis (Fig. 4 ; models without land cover) identified precipitation as the most influential predictor for both species. For A. variegatum , the maximum NDMI value ranked as the second most important predictor, whereas for R. microplus , the second most important was the amplitude of the second harmonic of night time land surface temperature (a2_LSTN). Slope was the third most important predictor for both species. In the A. variegatum model, additional predictors with intermediate permutation importance included minimum LSTN, minimum NDMI, the amplitude of the first harmonic of NDVI, and minimum LSTD. In the R. microplus model, elevation and maximum LSTD showed intermediate importance. All remaining predictors contributed minimally (< 5%) or not at all. The inclusion of landcover data significantly altered the importance of predictors, with Grass cover becoming the most influential variable for both tick species. The addition of landcover data improved predictive performance, as demonstrated by the ROC curve: the AUC increased from 0.794 to 0.839 for A. variegatum and from 0,781 and 0.833 for R. microplus models when land cover data were added (Fig. 5 ). These improvements justified the use of predicted suitability maps builded with landcover data in the subsequent MCDA-based risk model for E. ruminantium in Guadeloupe. Modelling E. ruminantium risk using MCDA Seven experts from France and South-Africa were surveyed to evaluate the relative importance of three core variables involved in E.ruminantium epidemiology. The host density layer received scores ranging from 5 to 9 (mean = 7.75, standard deviations = 1.48), the environmental suitability of A. variegatum from 8 to 10 (mean = 8.875, sd = 0.99) and the suitability of R. microplus from 1 to 10 (mean = 4.625, sd = 3.5). From these responses, the relative weights used in the MCDA were 0.36 for the host density layer, 0.42 for the A. variegatum suitability layer and finally 0.22 for the R. microplus suitability layer. Using these weights, risk maps for E. ruminantium were generated. The resulting predictions revealed moderate to high E.ruminantium risk in Marie Galante and Grande-Terre, reflecting the areas of highest environmental suitability for tick vectors and livestock presence. On the Basse Terre, risk was more spatially fragmented, with low predicted risk in central mountainous zones and higher risk in northern and coastal low-altitude areas. The island of La Désirade showed a moderate to high predicted risk, whereas the Les Saintes archipelago was associated with low to moderate levels of E.ruminantium risk. These patterns are visualized in Fig. 6 (left panel), which presents the spatial distribution of the risk indicator across the archipelago. The inclusion of land cover data in the MaxEnt-derived suitability layers led to a more fragmented risk pattern, especially aligning high-risk zones with grassland-dominated areas. However, the overall distribution trends observed in the “no land cover” model remained valid, with scattered high-risk patches still visible across the territory. The associated uncertainty, shown in Fig. 6 (right panel), was generally low (maximum standard deviation = 0.03), although slightly elevated in La Désirade when landcover data were excluded from the modelling process. The Grand-Fond sector, in the center of the southern part of Grande-Terre, showed moderate uncertainty, while on Basse-Terre Island, higher uncertainty was found in intermediate elevation zones. A “One at time” sensitivity analysis was used to evaluate how changes in individual input layers influenced risk predictions. The Mean Absolute Change Rate (MACR) was highest for the host density layer (MACR = 5.73% without land cover; 6.67% with land cover data), confirming its central role in driving risk (Fig. 7 ). The environmental suitability of A. variegatum was the second most influential predictor (MACR = 4.05% and 4.58%), followed by the R. microplus suitability (MACR = 3.65 and 2.44%). These findings are illustrated in Fig. 7 , which summarizes the sensitivity of the MCDA risk predictions to each variable under different modelling configurations. Interestingly, the model excluding land cover data yielded better validation metrics, with an AUC of 0.70, compared to 0.65 for the land cover inclusive version (Fig. 8 ). Discussion All inhabited islands of Guadeloupe were included in the sampling campaign. Amblyomma variegatum and Rhipicephalus microplus ticks were detected on animals across all islands, except La Désirade, where no ticks were found. Les Saintes appeared free of R. microplus . Serological evidence confirmed the circulation of Ehrlichia ruminantium throughout the whole Guadeloupean archipelago, highlighting the widespread exposure of local ruminant population. It is important to note that the western part of Basse-Terre was not sampled, mainly due to the low prevalence of livestock farming in this area. This is likely a consequence of its complex topography, dominated by steep slopes and narrow valleys, which make a large-scale animal husbandry less feasible. Similarly, few animals were sampled was observed in the southeast part of Basse-Terre, a region predominantly devoted to banana cultivation. In this region, only sheep were sampled, a host less attractive to adult A.variegatum adult 5 , 27 , potentially leading to false absences for this species (although R. microplus was detected). Furthermore, this area is also the wettest region of the island (see Fig. 1 ), with environmental conditions distinct from other parts in Guadeloupe. Given the limited number of animals sampled in these regions, models may have underestimated the environmental suitability for ticks. It is also worth mentioning that tick trapping is considered inefficient for A.variegatum and difficult to apply on large scale, as highlighted by Barré et al. (1997) 15 , who suggested that cattle represent the best “natural trap” for collecting ticks in Guadeloupe. The ENFA analysis revealed notable similarities between the ecological niches of Amblyomma variegatum and Rhipicephalus microplus , which was expected due to the significant spatial overlap in their occurrence records. This co-occurrence is well-supported in the literature, where Rhipicephalus ticks are frequently found in association with Amblyomma species 72 , 73 . Both ENFA analyses indicated high absolute marginality values for a common set of environmental variables, including temperature-related metrics, NDVI and NDMI indices, precipitation, elevation, and slope. These findings are consistent with previous studies on tropical cattle tick ecology, where similar environmental predictors have been successfully employed to study and model their ecological niches 74 – 77 . Among vegetation indices, NDMI consistently exhibited higher marginality values than NDVI across mean, minimum and maximum layers, suggesting that NDMI may be a more informative descriptor of moisture-related habitat conditions 78 . The ENFA also contributed to refining variable selection for subsequent habitat suitability modelling 51 . Variance Inflation Factor analysis revealed multicollinearity between several layers, leading to the exclusion of mean values in favour of minimum and maximum values, which are more likely to represent ecological thresholds and limiting factors for tick survival and distribution as previously suggested 79 – 81 . The inclusion of high-resolution land cover data did not alter the relative importance of the primary environmental variables identified by ENFA but revealed discrepancies in the environmental space occupied by each species, suggesting niche differentiation 62 . Among the land cover predictors, forest areas were associated with strong negative marginality, while grassland cover had positive marginality values for both species. These results reflect the origin of presence data, mostly collected in grazing and breeding areas where livestock are abundant. Both forest and grassland predictors were therefore retained for subsequent spatial modelling steps, for ecological relevance and discriminatory power. The MaxEnt models for both A. variegatum and R. microplus revealed strong ecological niche overlap, with high Pearson correlation coefficients between their predicted suitability layers (0.86 without land cover, 0.93 with land cover). This similarity mirrors their co-distribution patterns and shared environmental preferences, as previously described 62 , 73 , 82 , 83 . The highest suitability values for both species were predicted in Grande-Terre and Marie-Galante, areas with extensive livestock activity and favourable environmental conditions. In contrast, Basse-Terre displayed greater spatial heterogeneity. In the north, A. variegatum showed high to moderate suitability, whereas R. microplus was predicted with only moderate suitability. The pattern was reverse in the south, where R. microplus had higher suitability. In both species, the central mountainous region of Basse-Terre, which corresponds to the national park and is characterized by dense tropical vegetation, steep elevation and an absence of ruminant hosts, consistently showed low suitability. Similarly, mangrove areas along the coast exhibited low suitability, likely due to environmental conditions that are unfavourable for tick survival and host presence. Among the environmental predictors (excluding land cover), annual precipitation emerged as the most influential variable for both tick species, with permutation importance values of 24% for A. variegatum and 41% for R. microplus . The critical role of rainfall in shipping tick distribution is well- documented, as it directly influences tick survival, development, and host-seeking behaviour 84 , 85 . For A. variegatum , suitability dropped in areas with over 2000 mm of rainfall, while for R. microplus , the decline was less steep, suggesting greater tolerance to wetter conditions. These patterns correspond with previous observations 86 and may explain the higher suitability of R. microplus in the southern part of Basse-Terre. However, given the low number of animals sampled in this area, additional field data are needed to validate these trends. Finally, the sharp decline in suitability observed above 3000 mm/year, for both species, likely corresponds to the high-altitude rainforest of central Basse-Terre, where ruminant hosts are absent. The maximum NDMI and the amplitude of the second harmonic of LSTN emerged as the second most influential predictors for A. variegatum and R. microplus , respectively. Overall, A. variegatum was more influenced by moisture- and humidity-related variables, while R. microplus responded primarily to temperature-related factors. By integrating the predicted suitability maps, variable importance rankings, and response curves from models excluding land cover data, several ecological inferences can be drawn. In the northeastern region of Grande-Terre, habitat suitability diverged: A. variegatum showed medium suitability, while R. microplus reaches high suitability values. This region, characterized by hot, dry conditions and sparse vegetation, suggests greater desiccation tolerance in R. microplus . This assumptions is consistent with previous modelling efforts: Estrada-Pena, (2007) 17 low environmental suitability for A. variegatum in the arid northern part of Mexico, while Perez-Martinez et al. (2023) 20 found moderate to high suitability for R. microplus along dry eastern coasts in global-scale models using MaxEnt. Although spatialized relative humidity (RH) data were unavailable for Guadeloupe, despite RH being a key determinant of tropical tick survival 87 , its influence may be partially captured via correlated variables like temperature and precipitation 88 . NDMI, used here as proxy for environmental moisture, provided valuable information. In this context, the minimum NDMI (min_NDMI) displayed parabolic response curves for both species. A. variegatum exhibited optimum suitability near min_NDMI ≈ 0.2, while R. microplus favored drier conditions, with an optimum near NDMI ≈ 0, reinforcing its apparent greater resilience to aridity. In contrast, high NDMI values (both min and max layer) were associated with low predicted suitability for both species, conditions found in the forested, mountainous areas of central Basse-Terre, where livestock farming is prohibited. However, it is important to acknowledge the limits of macroscale models, as microhabitat-scale humidity conditions (e.g., shaded ground, dense vegetation cover, soil cracks) may provide suitable environments for tick survival, that are not captured at this spatial resolution 89 . Slope was the third most important predictor for both species (permutation importance: 19% for A. variegatum , 15% for R. microplus ). The response curves followed a similar pattern: both species displayed highest suitability at low to moderate slope values, declining at both extremes. Steep slopes may restrict livestock presence due to accessibility issues, while flat areas such as mangroves or sugarcane plantations, often unsuitable for livestock, were largely devoid of tick records 90 . Among temperature-related variables, minimum LSTN (min_LSTN) was the most influential predictor for A. variegatum (9%), while maximum daytime LST (max_LSTD) was most important for R. microplus (9%). All temperature predictors displayed decreasing response curves, indicating a preference for cooler environments. High temperatures likely increase desiccation risk, limiting tick survival. Notably, the max_LSTD response curve for A. variegatum declined linearly with increasing temperature, while for R. microplus , it remains stable up to 305°K, before sharply decreasing, suggesting a greater heat tolerance in this species. Including land cover data significantly altered predicted suitability maps and response curves, for both species, largely constraining areas of high suitability to grassland regions. This pattern was mainly driven by the "Grass" class, which had the highest permutation importance and strongly increasing response curves. Although this may suggest a potential model overfitting, since most presence points were in livestock-utilized grasslands 91 , it remains biologically justified. In Guadeloupe, domestic ruminants are the primary hosts of both tick species 27 , 92 , with wildlife infestations rare and generally incidental 93 , 94 . Therefore, high-resolution land cover data improves both the ecological realism and spatial precision of the models, as supported by improved validation metrics (see Fig. 5 ). R. microplus appears to occupy a broader ecological niche than A. variegatum in Guadeloupe, which is consistent with its wider global distribution. A factor that could explain this higher resistance to climatic variations could be the shorter free-living life of monoxenic R. microplus and egg incubation period if compared to three hosts ticks Amblyomma’s 95 . While A. variegatum is typically confined to tropical climates 17 , 35 , 92 , R. microplus has been reported across a more diverse range of environments worldwide 75 . Although R. microplus may not find optimal conditions throughout the entire Guadeloupean territory, the highest predicted suitability values (> 0.9) were concentrated in the eastern part of Marie-Galante. In contrast, A. variegatum exhibited very high suitability (> 0.9) across several areas, including Marie-Galante, Grande-Terre, and parts of Basse-Terre. These findings are consistent with field observations, where R. microplus infestations are less frequent than A. variegatum . The present models provide the first spatially explicit maps of the ecological niches of the two tick species representing a great threat in Guadeloupe’s livestock production system. The Multi-Criteria Decision Analysis (MCDA) approach was effectively employed to model the spatial risk of Ehrlichia ruminantium infection across the Guadeloupean archipelago. Models using tick suitability layers without land cover achieved an AUC of 0.702, while models including land cover had a lower AUC of 0.654, possibly reflecting overfitting to grassland areas. Although internal MaxEnt metrics improved with land cover, these models may offer less insight into actual disease dynamics. This suggests that simpler models may better reflect ecological realities in tick-borne disease modeling. These AUC values indicate moderate predictive performance 59 . However, validation procedure involved challenges. Infection status was assessed using the MAP1B serological test, which, though specific 96 , 97 , may underperform under endemic conditions 98 . Since MAP1B seropositivity can decline in frequently exposed animals 99 , a conservative validation approach was adopted: pixels were labeled negative only if at least three animals tested negative, in accordance with OIE Terrestrial Manual recommendations 100 . According to the literature on MCDA validation, more direct indicators of disease circulation, such as clinical case reports or molecular detection, would provide stronger support for risk model validation 25 , 101 . Unfortunately, such data are lacking in Guadeloupe due to limited surveillance of tick-borne diseases in livestock sector. Strengthening surveillance system for both tick infestation and tick borne diseases appears essential, especially in light of the vulnerability of the region to diseases’ spread across the Caribbean 102 . For example, during the winter of 2025, the Les Saintes islands, historically free of heartwater 103 , experienced several heartwater outbreaks (Giles Manuel, veterinary, personal communication). Our recent sampling confirmed serological evidences of infection in goats on the island 36 indicating an active risk of disease dissemination. Despite limitations, the overall methodology appears promising for assessing the risk and spatial distribution of tick-borne diseases like heartwater, especially under data-scarce conditions. Incorporating targeted epidemiological variables could further improve the accuracy of risk predictions. For example, Haoran et al. (2021) 104 demonstrated that proximity to recent clinical cases can serve as a valuable spatial predictor, while serological data on host protection could refine spatial estimates 105 . However, mapping host resistance, shaped by multiple factors 1 , requires extensive field data and robust surveillance. The resulting maps provided a meaningful proxy for heartwater disease which could be extended to other tick-borne disease risks in Guadeloupe due to the broad relation considered between the disease and the different risk layers used in the MCDA. To our knowledge, this constitutes the first spatially explicit risk assessment of Ehrlichia ruminantium in the region. Conclusion This study provides the first spatially explicit assessment of ecological suitability for Amblyomma variegatum and Rhipicephalus microplus , along with the first effort to map heartwater risk in Guadeloupe. By combining environmental predictors and species distribution models within a multi-criteria decision analysis framework, we produced informative maps that highlight key areas of vector suitability and potential disease risk. Despite limitations, particularly in sampling coverage and the absence of disease-specific indicators, our approach aligns with existing ecological knowledge and field observations. The resulting maps offer a valuable operational tool for local stakeholders, enabling prioritization of field investigations and guiding surveillance efforts. Notably, ecologically distinct zones such as southern Basse-Terre warrant further exploration due to their environmental heterogeneity and potential under-sampling. Future risk assessments could benefit from the integration of serological, molecular, or clinical data to refine model accuracy and resolution. Expanding such efforts would not only enhance our understanding of disease dynamics but also support more targeted and effective control strategies. Beyond the Guadeloupean context, the methodological framework proposed here is transferable to other Caribbean islands or tropical regions facing similar data constraints. It offers a scalable and practical approach to support surveillance planning and strengthen animal health systems in vulnerable territories affected by tick-borne diseases. Declarations Data availability Tick presence data are not publicy available due to confidentiality concerns, as they were collected from livestock and include geolocated information obtained with the consent of individual farmers. Requests for access to these data can be considered on a case-by-case basis by the corresponding author, subject to appropriate data-sharing agreements. All environmental raster datasets used in the modelling process are publicy available from the following sources: Topographic data: BD-TOPO, IGN - https://geoservices.ign.fr/bdtopo Meteorological data: Météo France - https://meteo.data.gouv.fr/datasets/donnees-climatologiques-de-base-quotidiennes/ MODIS imagery: MODIStsp R package - https://github.com/ropensci/MODIStsp High-resolution landcover data : Karucover 2022 - https://catalogue.karugeo.fr/geonetwork/srv/fre/catalog.search#/metadata/ee390aa5-9eb8-4f9f-94da-6833d25662a Acknowledgements We thank all participants of the TISARU project, from farmers to scientific experts, for their valuable contributions. We are especially grateful to the SANIGWA association for their assistance in collecting tick presence and cattle serology data. Author contribution LG; EE: Conceptualization/design, investigation, formal analysis, methodology, writing, writing ± review, validation, data curation, supervision. VD : Conducted field surveys, formal analysis, software, drafting the initial manuscript, validation. All authors corrected and approved the submitted version of the manuscript. Additional information Competing interests The authors declare no competing interests. Funding This work was supported by the United States Department of Agriculture (USDA) under grant number 58-3022-1-018-F (Risk of Arthropod-borne diseases in the Caribbean; RACE). The authors also acknowledge the support of the Guadeloupe region and European Agricultural Fund for Rural Development (EAFRD) through the TISARU project (FEADER_M16_2021_01), which provided logistical support for animal sampling activities. Ethics declarations Tick presence and cattle serological data were collected as part of survey conducted under the RACE and TISARU projects. The study protocol was reviewed and approved by the relevant ethical authority, under APAFIS approval number #43250-2023050210511531 v3, prior to implementation. 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Camus, E. et al. Heartwater in Guadeloupe and in the Carribbean. Revue d’Elevage et de Médecine Vétérinaire des. Pays Tropicaux . 46 , 109–114 (1993). Haoran, W. et al. Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis. BMC Vet. Res. 17 , 374 (2021). Gopalakrishnan, B., Sugumaran, M. P., Balaji, K., Thirunavukkarasu, M. & Davamani, V. GIS-based approach for mapping the density and distribution of crossbred cattle. Indian J. Anim. Sci 91 , (2021). Additional Declarations No competing interests reported. Supplementary Files Suplementary.docx Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Oct, 2025 Reviews received at journal 26 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 26 Aug, 2025 Editor invited by journal 26 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 08 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7142010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":506943198,"identity":"fcb92c53-2a5c-47a0-be4a-29b822d79f69","order_by":0,"name":"Victor Dufleit","email":"","orcid":"","institution":"ASTRE, Université Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Dufleit","suffix":""},{"id":506943199,"identity":"4b0598de-58b7-4ff4-aee3-cfe89269bc43","order_by":1,"name":"Eric Etter","email":"","orcid":"","institution":"ASTRE, Université Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Etter","suffix":""},{"id":506943200,"identity":"d0a3a51b-0e46-444f-819c-926a71520e04","order_by":2,"name":"Laure Guerrini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPmTOAQYDGwY2IEOCwQa3FjY0LWlQLWlEagGCw2ASvxb2M2YfPjDUJvZLn314uKDgvDyf9OGHNxgS7uHWwpNjPHMGw/HEmX3pBodnGNw2bONLM7ZgSCjG47AcY2YehmOJG86wMRzmMbidwMbDYCbB+CMBtxb+N8bMf4Ba9kO0nANqYf8mwZCAR4sE0BYGhprEDTxgLQeAWnjMCGh5VszYY3DAeAbElmTDNh6eYosEPFr4+ZM3M/yoqJPt72Fj/szzx05evod9440PeLRAgMFhNAFCGoCgjrCSUTAKRsEoGLkAAOZXRenQlUCIAAAAAElFTkSuQmCC","orcid":"","institution":"ASTRE, Université Montpellier","correspondingAuthor":true,"prefix":"","firstName":"Laure","middleName":"","lastName":"Guerrini","suffix":""}],"badges":[],"createdAt":"2025-07-16 16:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7142010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7142010/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30181-4","type":"published","date":"2025-12-01T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90533355,"identity":"df9285f0-f2a1-4e01-9859-681b6231167b","added_by":"auto","created_at":"2025-09-03 19:04:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":587531,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical overview of the Guadeloupe archipelago. The background layer shows predicted cattle density, generated using a census disaggregation methodology as described in Dufleit et al, 2025a.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/059cacc70b979ddcce4c4ff7.png"},{"id":90533772,"identity":"c2aa036c-32fa-4bd3-bacc-66209f5d84f3","added_by":"auto","created_at":"2025-09-03 19:12:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":530222,"visible":true,"origin":"","legend":"\u003cp\u003eEcological Niche Factor Analysis (ENFA) results for Amblyomma variegatum and Rhipicephalus microplus. The graph represents the factor loadings for each environment variable along the marginality and specialisations axes. Axes have been omitted, and values rescaled to enhance visual clarity.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/a1e1415782c602b1abc80e65.png"},{"id":90533359,"identity":"e3d2f138-a51d-490a-9047-3a428c1b5448","added_by":"auto","created_at":"2025-09-03 19:04:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1579791,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted ecological suitability of A.variegatum and R.microplus in Guadeloupe, based on optimized MaxEnt models.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/793834a3bf433ac479db7920.png"},{"id":90533774,"identity":"9d4d208b-048e-45d3-8c59-592052325cda","added_by":"auto","created_at":"2025-09-03 19:12:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":432753,"visible":true,"origin":"","legend":"\u003cp\u003ePermutation importance of environmental predictors in MaxEnt models for both tick species.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/344aadded0bc7ea16a49dc42.png"},{"id":90533362,"identity":"4404040a-769a-457b-b377-ffb7ee3533bb","added_by":"auto","created_at":"2025-09-03 19:04:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":156869,"visible":true,"origin":"","legend":"\u003cp\u003eeceiver Operating Characteristic (ROC) curves and corresponding Area Under the Curve (AUC) values for MaxEnt models.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/4b131e2cc7ba8157ce6ede17.png"},{"id":90533775,"identity":"1f035985-5cdc-448a-8f4a-db5645103e9f","added_by":"auto","created_at":"2025-09-03 19:12:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1789651,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Heartwater risk map generated via Multi-Criteria Decision Analysis (MCDA). Right : Associated uncertainty map derived from a One-at-a-Time (OAT) sensitivity analysis\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/03f4238ada0dbec47123dddc.png"},{"id":90533779,"identity":"b06de1a4-f077-4950-8626-95a6309f1ab8","added_by":"auto","created_at":"2025-09-03 19:12:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":157281,"visible":true,"origin":"","legend":"\u003cp\u003eMean Absolute Change Rate (MACR) from the One-at-a-Time (OAT) sensitivity analysis, illustrating the relative influence of each predictor layer on final heartwater risk estimates generated by the MCDA model.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/1a500238dcc4e49987f2dc3c.png"},{"id":90533976,"identity":"849cabe5-c943-4277-b485-98ed0ac5a928","added_by":"auto","created_at":"2025-09-03 19:20:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":95984,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves and corresponding Area Under the Curve (AUC) values assessing the predictive performance of the MCDA models with and without inclusion of land cover data.\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/294683cc9dad04c1c6a1272a.png"},{"id":97724837,"identity":"32e5ccf0-683f-4fb3-9189-ddde5d7daeee","added_by":"auto","created_at":"2025-12-08 16:13:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5682334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/f6fef183-212b-4362-85e5-e0f25105ab6d.pdf"},{"id":90533974,"identity":"14e6b914-abcc-4698-b8c2-8fbe8f362c32","added_by":"auto","created_at":"2025-09-03 19:20:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2225769,"visible":true,"origin":"","legend":"","description":"","filename":"Suplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7142010/v1/593e483c522f12e6df3df7ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping heartwater risk in Guadeloupe: a combination of spatial modelling approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeartwater is an acute, often fatal tick-borne disease of ruminants caused by \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, with significant economic impacts in affected regions\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In sub-Saharan Africa and the Caribbean, ticks of the genus \u003cem\u003eAmblyomma\u003c/em\u003e are recognized as the primary vectors of the disease\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In Guadeloupe, the pathogen was first detected in 1980\u003csup\u003e6\u003c/sup\u003e, likely introduced via cattle imported from West Africa infested with \u003cem\u003eAmblyomma variegatum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. To date, \u003cem\u003eA. variegatum\u003c/em\u003e remains the only confirmed vector in the region. However, recent studies from West Africa\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e suggest that \u003cem\u003eRhipicephalus microplus\u003c/em\u003e, another widespread cattle tick also present in Guadeloupe, may play a role in \u003cem\u003eE. ruminantium\u003c/em\u003e transmission. Already established as a vector of anaplasmosis and babesiosis in Guadeloupe\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, the presence of both tick species represents a substantial challenge for livestock health, especially in a context of declining production\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEcological niche modelling has proven useful in identifying the potential distribution of vector species using environmental predictors\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Among the available methods, maximum entropy modelling (MaxEnt) is widely used for its ability to work with presence-only data, an advantage for species such as \u003cem\u003eA. variegatum\u003c/em\u003e, which are difficult to detect or sample consistently using traps\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. MaxEnt has been successfully applied to model the potential distribution of \u003cem\u003eA. variegatum\u003c/em\u003e in the Americas\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and \u003cem\u003eR. microplus\u003c/em\u003e on both global and regional scales\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn parallel, spatial risk assessment methods such as Multi-Criteria Decision Analysis (MCDA) have been used to map vector-borne disease risk by integrating environmental and epidemiological data layers \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. While various methods are available for diseases risk mapping\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, MCDA enables the integration and weighting of diverse predictor layers, such as host density, climate, and vegetation, based on expert knowledge or literature-derived evidence\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This method has shown particular relevance for diseases whose transmission is influenced by multiple interacting environmental factors\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite existing data on tick infestations and \u003cem\u003eE. ruminantium\u003c/em\u003e circulation\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, no comprehensive spatial modelling study has yet been conducted in Guadeloupe. This study aims to fill that gap by introducing an integrative framework for spatial heartwater risk mapping. We combine field-collected presence data for \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e, serological data, environmental predictors derived from remote sensing, and disaggregated cattle density data\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Key predictors were first identified using Ecological Niche Factor Analysis (ENFA)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and then integrated into MaxEnt models. The resulting tick suitability maps were integrated into an MCDA to produce the first spatially explicit heartwater risk maps for the Guadeloupe archipelago.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cp\u003eStudy area\u003c/p\u003e\u003cp\u003eGuadeloupe is an archipelago located in the Lesser Antilles in the Caribbean and constitutes an overseas region of France. It comprises several islands, five of which are inhabited. The two largest are Basse-Terre to the west and Grande-Terre to the east. The archipelago also includes three administrative dependencies: La D\u0026eacute;sirade (northeast of Grande-Terre), Marie-Galante (south of Grande-Terre), and Les Saintes (southeast of Basse-Terre). The region is characterized by a tropical climate with notable spatial variation. Basse-Terre, which hosts the Soufri\u0026egrave;re volcano and the island\u0026rsquo;s highest elevations, experiences significantly higher annual rainfall compared to the rest of the archipelago. This disparity is largely due to orographic effects, as the mountainous terrain intercepts the easterly trade winds and moisture-laden clouds, resulting in a much wetter climate, particularly in southern Basse-Terre\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a geographical overview of Guadeloupe, with predicted cattle density shown as a background layer. These density estimates, extracted from a prior study based on a census disaggregation methodology\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, were a key input for the risk mapping analyses conducted in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eData\u003c/p\u003e\u003cp\u003eVector presence and heartwater serological evidence\u003c/p\u003e\u003cp\u003eTick occurrence data were collected for the two main tick species infesting ruminants in Guadeloupe: \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e. Ticks were obtained through physical examination of cattle, sheep, and goats, following the standardized protocol described by Stachurski (2000)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Simultaneously, blood samples were collected during animal handling for serological testing of \u003cem\u003eE. ruminantium\u003c/em\u003e antibodies using the MAP1B ELISA assay. Further details on the sampling strategy and laboratory protocols are available in\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor spatial modelling, tick presence data, defined as the locations of infested animals, were projected onto a 225 m resolution raster grid\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, consistent with previous cattle density mapping in the region\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. To validate the MCDA-based risk assessment, a binary raster layer representing the presence or absence of \u003cem\u003eE. ruminantium\u003c/em\u003e exposure was also produced from the serological dataset.\u003c/p\u003e\u003cp\u003eInitial compilation of predictors\u003c/p\u003e\u003cp\u003eTo support ecological niche modelling of \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e in Guadeloupe, 32 environmental raster layers were compiled to characterize habitat conditions relevant to tick ecology\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTopographic layers: Elevation data were provided by BRGM Guadeloupe (Bureau de Recherche G\u0026eacute;ologiques et Mini\u0026egrave;res), from which a slope raster layer was derived using QGIS.\u003c/p\u003e\u003cp\u003eClimatic layers: Long-term precipitation data (1990\u0026ndash;2022) were retrieved from M\u0026eacute;t\u0026eacute;o-France (2025). Median annual precipitation values were calculated and interpolated into a continuous raster surface via ordinary kriging (via the gstat R package\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e), resulting in an annual precipitation raster.\u003c/p\u003e\u003cp\u003eLand cover layers: High-resolution land cover information was extracted from the Karucover 2022 database\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, using the fourth-level classification. The following adjustments were applied:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Polygons classified as \u0026ldquo;Built\u0026rdquo;, \u0026ldquo;Non-built\u0026rdquo;, \u0026ldquo;Mineral materials zone,\u0026rdquo; and \u0026ldquo;Composite material zone\u0026rdquo; were merged into a single \u0026ldquo;Artificial areas \u0026ldquo;category.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- \u0026ldquo;Stone\u0026rdquo;, \u0026ldquo;Rock\u0026rdquo;, and \u0026ldquo;Sand/silt\u0026rdquo; classes were grouped under \u0026ldquo;Stone/sediment\u0026rdquo; category.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eProportions of each land cover class were calculated within raster pixels to produce nine continuous raster layers representing land cover predictors.\u003c/p\u003e\u003cp\u003eRemote sensing layers: Environmental variables derived from MODIS products were processed using the MODIStsp R package\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The selected datasets included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Land surface temperature (LST) for both day and night at 1 km resolution\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Surface reflectance bands at 500 m resolution: Band 1 (Red), Band 2 (Near Infrared), and Band 6 (Short-Wave Infrared)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLST data were filtered using quality assurance flags (\"QAday\" and \"QAnight\"), while surface reflectance data were masked using \"state_cloud\" and \"state_int_cld\" filters. A land mask based on the departmental boundary of Guadeloupe was applied to all remote sensing products.\u003c/p\u003e\u003cp\u003eTwo vegetation indices were derived from the surface reflectance data:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Normalized Difference Vegetation Index (NDVI): a proxy for vegetation greenness and productivity\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Normalized Difference Moisture Index (NDMI): an indicator of surface moisture\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTime series for four key indicators (LST_Day, LST_Night, NDVI, NDMI) from 2019 to 2024 were processed using Fourier analysis, following the method of Scharlemann et al., (2008)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. For each variable, statistical summaries\u0026mdash;including mean, minimum, maximum, and amplitudes of the first and second harmonics\u0026mdash;were extracted for inclusion in the models.\u003c/p\u003e\u003cp\u003eAll raster layers were resampled to a common spatial resolution of 225 m using bilinear interpolation\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, to ensure spatial consistency with cattle density data and modelling framework.\u003c/p\u003e\u003cp\u003eTo address data availability constraints in other Caribbean regions, two predictor sets were prepared: one including the Karucover-derived land cover layers, and another excluding them. This dual approach enabled an evaluation of the added predictive value of high-resolution land cover data in improving model performance.\u003c/p\u003e\u003cp\u003ePredictor selection via Ecological Niche Factor Analysis (ENFA)\u003c/p\u003e\u003cp\u003eTo guide predictor selection and understand species-environment relationships, Ecological Niche Factor Analysis (ENFA) was conducted using the adehabitatHS R package\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. ENFA is a presence-only multivariate method that compares environmental conditions at species presence locations with the background environmental space of the study area\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Two key metrics were extracted:\u003c/p\u003e\u003cp\u003eMarginality: quantifies the degree to which the environmental conditions at species presence locations deviate from the average conditions across the study area. A high marginality value indicates that the species occurs in environments that differ significantly from the general landscape\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSpecialization: reflects the degree to which a species is restricted to a narrow range of environmental conditions compared to the total available environmental space. Higher values indicate a more constrained ecological niche.\u003c/p\u003e\u003cp\u003eENFA results were visualized using factor maps, aiding both in the interpretation of ecological patterns and in guiding predictor selection. Predictors with significantly high absolute marginality values were retained for subsequent modelling steps, as these likely represent key ecological determinants of tick occurrence\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo control for multicollinearity among the retained predictors, Variance Inflation Factor (VIF) analysis was applied iteratively\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Predictors with VIF values greater than 10 were considered highly collinear; in such cases, ecologically meaningful variables were retained based on the specialization metric and prior literature and expert knowledge. The process was repeated until all included predictors had VIF values below the threshold of 10\u003csup\u003e53\u003c/sup\u003e, ensuring a parsimonious yet ecologically informative set of covariates for modeling.\u003c/p\u003e\u003cp\u003eNiche prediction using MaxEnt\u003c/p\u003e\u003cp\u003eThe Maximum Entropy (MaxEnt) modeling framework was selected due to the nature of the species occurrence data, which consisted exclusively of presence-only records\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This approach is particularly appropriate in contexts where absence data are unreliable or unavailable, such as in the case of \u003cem\u003eAmblyomma variegatum\u003c/em\u003e, for which no effective trapping technique currently exists\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Instead, presence data were obtained via direct inspection of ruminants during field surveys, with GPS coordinates recorded for each infested host. This method, previously recommended in Guadeloupe\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, may not reliably capture tick absence, as negative information may result from transient factors like recent acaricide treatments rather than true environmental exclusion.\u003c/p\u003e\u003cp\u003eA central feature of MaxEnt is its use of background points (pseudo-absence), which represent the range of environmental conditions across the study area where the species was not recorded\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The algorithm estimates the potential ecological niche by identifying the probability distribution of environmental conditions that maximizes entropy, that is, the most uniform distribution possible, subject to constraints imposed by the environmental characteristics at known presence locations.\u003c/p\u003e\u003cp\u003eModel tuning was conducted using the \u003cem\u003eENMeval\u003c/em\u003e R package\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e with focus on optimizing two key parameters: the regularization multiplier (RM) and feature classes (FC). The RM, also referred to as the β-parameter, governs the model complexity by penalizing overfitting, higher values result in smoother, more generalized predictions. RM values ranging from 1 to 5 were tested. Feature classes specify the allowable relationships between predictors and species occurrence, and combinations of linear, quadratic, hinge, and product features were evaluated. \u0026ldquo;Linear only\u0026rdquo; models were excluded to avoid oversimplification, and \u0026ldquo;threshold\u0026rdquo; feature were omitted to prevent abrupt spatial changes in suitability predictions. The optimal model configuration was selected based on the lowest corrected Akaike Information Criterion (AICc)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e value among all candidate models.\u003c/p\u003e\u003cp\u003eMaxEnt models were developed for each tick species using the occurrence records collected during field surveys:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eAmblyomma variegatum\u003c/em\u003e: 196 presence records aggregated into 105 unique raster pixels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eRhipicephalus microplus\u003c/em\u003e: 132 presence records aggregated into 74 unique pixels.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eEnvironmental predictors previously selected through ENFA were included in the modelling. For both species, 10,000 background points were randomly generated. The \u0026ldquo;block\u0026rdquo; method of spatial partitioning was applied to separate training and testing subsets for cross-validation. Model fitting was performed using the \u0026lsquo;maxent.jar\u0026rsquo; implementation as described by Phillips et al. (2017)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. From the final best-fitting models, variable importance metrics and response curves were extracted to assess the influence of individual predictors on species distribution. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve, and the Area Under the Curve (AUC) statistic, providing a quantitative measure of discriminatory power\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSpatial multicriteria decision analysis to estimate \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e risk\u003c/p\u003e\u003cp\u003eTo assess the spatial risk of \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e infection, composite risk maps were developed by integrating the environmental suitability maps for the two tick vectors \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e with a host density raster layer, using a Multi-Criteria Decision Analysis (MCDA) approach\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Although the role of \u003cem\u003eR. microplus\u003c/em\u003e in the transmission of \u003cem\u003eE. ruminantium\u003c/em\u003e has only been confirmed under experimental conditions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, this vector was included in the model to reflects its ecological presence and potential epidemiological relevance. The MCDA process incorporated a weighting mechanism to modulate the contribution of \u003cem\u003eR. microplus\u003c/em\u003e relative to \u003cem\u003eA. variegatum\u003c/em\u003e. The livestock density map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was produced through spatial disaggregation of municipal-level census data following the Gridded Livestock of the World (GLW) methodology\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, as modified for Guadeloupe by\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. While previous studies have proposed risk factors for \u003cem\u003eE.ruminantium\u003c/em\u003e infection\u003csup\u003e\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, many are constrained by limited spatial resolution or regional specificity. Therefore, the present risk model was structured around three spatially explicit layers, reflecting vectors and host dynamics: livestock density and environmental suitability for both \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn line with standard MCDA procedures, each input raster was transformed using a function reflecting hypothesized relationships with disease risk. Due to limited quantitative data describing the direct association between \u003cem\u003eE. ruminantium\u003c/em\u003e transmission and specific environmental or host-related factors, an increasing linear transformation was applied to all three layers, under the assumption that risk increases proportionally with both host density and tick vector suitability.\u003c/p\u003e\u003cp\u003eTo assign relative weights to the three risk factors, a structured expert elicitation was conducted, following the guidelines outlined by Greene et al., (2011)\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Subject-matter expert were asked to score each factor on a scale from 1 to 10, reflecting its perceived importance in \u003cem\u003eE. ruminantium\u003c/em\u003e transmission dynamics. These scores were then aggregated and normalized to a 0 \u0026minus;\u0026thinsp;1 scale, ensuring that the sum of all weights equaled 1\u003csup\u003e67\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinal risk scores were calculated using a weighted linear combination of the three transformed raster layers\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Model validation was performed using ROC curve analysis and AUC metrics, based on presence/absence data from serological testing\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Each pixel was assigned a value of 1 if serological evidence of \u003cem\u003eE. ruminantium\u003c/em\u003e exposure was found in the corresponding sampled location, and 0 otherwise. Following recommendations from the WOAH terrestrial manual statement\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, MAP1B test should only be considered at herd level to describe presence or absence of \u003cem\u003eE.ruminantium\u003c/em\u003e. A pixel was considered seronegative only if at least three animals sampled within that pixel tested negative. Pixels that did not meet this threshold were excluded from the validation dataset to reduce false negatives.\u003c/p\u003e\u003cp\u003eTo assess model robustness, a \"one-at-a-time\" (OAT) sensitivity analysis was performed\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. In this approach, the weight of each input factor was incrementally varied by \u0026plusmn;\u0026thinsp;0.2 across 40 simulation steps, while proportionally adjusting the remaining weights to maintain a total weight sum of 1. For each iteration, the average absolute rate of change in the predicted risk values was computed. An uncertainty map was subsequently produced, defined as the standard deviation of the predicted values across all sensitivity scenarios.\u003c/p\u003e\u003cp\u003eAll spatial MCDA procedures were implemented using the \u003cem\u003espatMCDA\u003c/em\u003e R package\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, and model evaluation metrics (ROC and AUC) were computed using the \u003cem\u003epROC\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003eand compliance\u003c/p\u003e\u003c/p\u003e\u003cp\u003eTick presence and cattle serological data were collected as part of survey conducted under the RACE and TISARU projects. The study protocol was reviewed and approved by the relevant ethical authority, under APAFIS approval number #43250-2023050210511531 v3, prior to implementation. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTick presence and \u003cem\u003eErlichia ruminantium\u003c/em\u003e serological evidence\u003c/p\u003e\u003cp\u003eAll inhabited islands of the Guadeloupe archipelago were surveyed during the field campaign. Blood sampling confirmed the presence of \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e infection across all islands\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. A total of 421 animals were sampled, including 261 cattle, 135 goats, and 25 sheep. Serological analysis using the MAP1-B ELISA assay identified 103 animals as positive for \u003cem\u003eE. ruminantium\u003c/em\u003e antibodies, including 76 cattle and 27 goats\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRegarding tick infestation, 189 animals (44%) were found to be infested with \u003cem\u003eAmblyomma variegatum\u003c/em\u003e, 130 animals (30%) with \u003cem\u003eRhipicephalus microplus\u003c/em\u003e, and 92 animals (22%) were co-infested with both tick species. Among the 227 animals infested with at least one tick species (54% of the total sample), co-infestation represented approximately 40% of cases. No ticks were detected on inspected animals in La D\u0026eacute;sirade, and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e was absent from the islands of Les Saintes.\u003c/p\u003e\u003cp\u003eAfter spatial projection onto a 225-meter resolution grid, the 189 and 130 tick presence records were aggregated into 105 and 74 presence pixels for \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e, respectively. Of these, 64 pixels indicated co-occurrence of both tick species. For \u003cem\u003eE. ruminantium\u003c/em\u003e presence/absence data used in MCDA model validation, animal sampling data were summarized across 157 pixels locations. Among these, 64 pixels exhibited seropositive results in at least one animal. The remaining pixels were characterized by at least three animals with negative test results.\u003c/p\u003e\u003cp\u003eVectors\u0026rsquo; Ecological niches\u003c/p\u003e\n\u003ch3\u003eENFA\u003c/h3\u003e\n\u003cp\u003eEcological niche factor analysis realised for both \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e revealed broadly similar environmental preferences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Temperature-related variables, including mean, minimum, and maximum values, as well as day and night land surface temperatures (LSTD, LSTN), along with the first harmonic amplitude (a1_LSTN), NDVI, NDMI, and amplitude of second harmonics of NDMI (a2_NDMI) displayed high positive marginality in both species. Specifically, \u003cem\u003eA. variegatum\u003c/em\u003e showed a particularly strong association with a1_LSTD, a pattern not observed for \u003cem\u003eR. microplus\u003c/em\u003e. In contrast, both species exhibited strong negative marginality for precipitation, elevation, slope and NDMI-related variables (mean, min and max), which consistently surpassed their NDVI counterparts. A distinguishing feature of \u003cem\u003eR. microplus\u003c/em\u003e was a strong negative marginality for the second harmonic amplitude of night temperature (a2_LSTN), absent in \u003cem\u003eA. variegatum\u003c/em\u003e. Additionally, high-resolution land cover variables, especially the proportions of \u0026ldquo;Grass\u0026rdquo; and \u0026ldquo;Forest\u0026rdquo; classes, exerted marked influence on niche definition for both species.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMaxEnt modelling results\u003c/p\u003e\u003cp\u003eBased on the ENFA results, an initial set of 18 environmental predictors was selected for ecological niche modelling of \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e. These included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Statistical summaries: mean, minimum, and maximum values of LSTD, LSTN, NDMI and NDVI;- Fourier components: first harmonic amplitudes of NDMI, NDVI and LSTN, and the second harmonic amplitude of NDMI;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Topographic variables: elevation and slope;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Precipitation: annual cumulative precipitation;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Species-specific variables: the first harmonic amplitude of LSTD for \u003cem\u003eA. variegatum;\u003c/em\u003e the second harmonic amplitude of LSTN for \u003cem\u003eR. microplus\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn models including land cover data, the predictors of \u0026ldquo;Forest\u0026rdquo; and \u0026ldquo;Grass\u0026rdquo; cover classes were added, based on their high marginality values identified via ENFA.\u003c/p\u003e\u003cp\u003eAnalysis of VIF exhibited significant multicollinearity among several predictors (VIF\u0026thinsp;\u0026gt;\u0026thinsp;10). Consequently, mean values of LSTD, LSTN, NDMI, and NDVI were removed, as minimum and maximum values were considered more relevant for capturing ecological constraints. Additionally, maximum LSTN and first harmonic amplitude of NDMI were excluded due to persistent collinearity with elevation.\u003c/p\u003e\u003cp\u003eAfter variable reduction, 12 predictors were retained in the baseline models, and 14 predictors in the models incorporating land cover data.\u003c/p\u003e\n\u003ch3\u003eModel tuning and performance\u003c/h3\u003e\n\u003cp\u003eOptimal MaxEnt models for \u003cem\u003eA. variegatum\u003c/em\u003e used a regularization multiplier (β) of 2 (without land cover) and 3 (with land cover), employing only linear and quadratic functions to represent the effects of predictors on suitability. For \u003cem\u003eR. microplus\u003c/em\u003e, the best models were obtained with β\u0026thinsp;=\u0026thinsp;5, both with and without landcover data. In the model without land cover, linear, quadratic, hinge and product feature classes were selected. In the model with land cover data, the product feature class was not included in the optimal configuration. Response curves for the predictors included in each model are provided in supplementary materials SM1-4.\u003c/p\u003e\n\u003ch3\u003eModel predictions\u003c/h3\u003e\n\u003cp\u003ePredicted habitat suitability maps from the best-fitted models are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The spatial predictions of the two tick species presented a Pearson correlation coefficient of 0.86 (without land cover) and 0.93 (with landcover), both statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDespite the high correlation observed between the ecological niches of \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e, the predicted spatial distribution patterns revealed notable differences. Both species showed highest environmental suitability in Marie-Galante. However, \u003cem\u003eA. variegatum\u003c/em\u003e, also exhibited similarly high suitability values across the plains of Grande-Terre, whereas \u003cem\u003eR. microplus\u003c/em\u003e showed broadly high\u0026mdash;but not peak\u0026mdash;suitability in the same region. On Basse-Terre, \u003cem\u003eA. variegatum\u003c/em\u003e had medium to high suitability in the northeast zones and isolated pockets in the southwest. \u003cem\u003eR. microplus\u003c/em\u003e displayed a comparable distribution but slightly higher suitability in the southeastern part of the island. For both species, the central mountainous area of Basse-Terre consistently showed low environmental suitability. In Les Saintes, \u003cem\u003eA. variegatum\u003c/em\u003e was associated with low to medium suitability, while \u003cem\u003eR. microplus\u003c/em\u003e exhibited medium to high suitability values.\u003c/p\u003e\u003cp\u003eThe inclusion of land cover data as a predictor significantly influenced model outputs by constraining high suitability areas to grassland zones. Nonetheless, the patterns observed in the models without land cover data remained consistent with the revised predictions. With land cover included, the highest suitability values (\u0026gt;\u0026thinsp;0.9) for \u003cem\u003eR. microplus\u003c/em\u003e were predicted in both Marie-Galante and Grande-Terre. Such a modifications was not observed for \u003cem\u003eA. variegatum\u003c/em\u003e.\u003c/p\u003e\u003cp\u003ePermutation importance analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; models without land cover) identified precipitation as the most influential predictor for both species. For \u003cem\u003eA. variegatum\u003c/em\u003e, the maximum NDMI value ranked as the second most important predictor, whereas for \u003cem\u003eR. microplus\u003c/em\u003e, the second most important was the amplitude of the second harmonic of night time land surface temperature (a2_LSTN). Slope was the third most important predictor for both species. In the \u003cem\u003eA. variegatum\u003c/em\u003e model, additional predictors with intermediate permutation importance included minimum LSTN, minimum NDMI, the amplitude of the first harmonic of NDVI, and minimum LSTD. In the \u003cem\u003eR. microplus\u003c/em\u003e model, elevation and maximum LSTD showed intermediate importance. All remaining predictors contributed minimally (\u0026lt;\u0026thinsp;5%) or not at all.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe inclusion of landcover data significantly altered the importance of predictors, with Grass cover becoming the most influential variable for both tick species. The addition of landcover data improved predictive performance, as demonstrated by the ROC curve: the AUC increased from 0.794 to 0.839 for \u003cem\u003eA. variegatum\u003c/em\u003e and from 0,781 and 0.833 for \u003cem\u003eR. microplus\u003c/em\u003e models when land cover data were added (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These improvements justified the use of predicted suitability maps builded with landcover data in the subsequent MCDA-based risk model for \u003cem\u003eE. ruminantium\u003c/em\u003e in Guadeloupe.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModelling \u003cem\u003eE. ruminantium\u003c/em\u003e risk using MCDA\u003c/p\u003e\u003cp\u003eSeven experts from France and South-Africa were surveyed to evaluate the relative importance of three core variables involved in \u003cem\u003eE.ruminantium\u003c/em\u003e epidemiology. The host density layer received scores ranging from 5 to 9 (mean\u0026thinsp;=\u0026thinsp;7.75, standard deviations\u0026thinsp;=\u0026thinsp;1.48), the environmental suitability of \u003cem\u003eA. variegatum\u003c/em\u003e from 8 to 10 (mean\u0026thinsp;=\u0026thinsp;8.875, sd\u0026thinsp;=\u0026thinsp;0.99) and the suitability of \u003cem\u003eR. microplus\u003c/em\u003e from 1 to 10 (mean\u0026thinsp;=\u0026thinsp;4.625, sd\u0026thinsp;=\u0026thinsp;3.5).\u003c/p\u003e\u003cp\u003eFrom these responses, the relative weights used in the MCDA were 0.36 for the host density layer, 0.42 for the \u003cem\u003eA. variegatum\u003c/em\u003e suitability layer and finally 0.22 for the \u003cem\u003eR. microplus\u003c/em\u003e suitability layer. Using these weights, risk maps for \u003cem\u003eE. ruminantium\u003c/em\u003e were generated.\u003c/p\u003e\u003cp\u003eThe resulting predictions revealed moderate to high \u003cem\u003eE.ruminantium\u003c/em\u003e risk in Marie Galante and Grande-Terre, reflecting the areas of highest environmental suitability for tick vectors and livestock presence. On the Basse Terre, risk was more spatially fragmented, with low predicted risk in central mountainous zones and higher risk in northern and coastal low-altitude areas. The island of La D\u0026eacute;sirade showed a moderate to high predicted risk, whereas the Les Saintes archipelago was associated with low to moderate levels of \u003cem\u003eE.ruminantium\u003c/em\u003e risk. These patterns are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (left panel), which presents the spatial distribution of the risk indicator across the archipelago. The inclusion of land cover data in the MaxEnt-derived suitability layers led to a more fragmented risk pattern, especially aligning high-risk zones with grassland-dominated areas. However, the overall distribution trends observed in the \u0026ldquo;no land cover\u0026rdquo; model remained valid, with scattered high-risk patches still visible across the territory. The associated uncertainty, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (right panel), was generally low (maximum standard deviation\u0026thinsp;=\u0026thinsp;0.03), although slightly elevated in La D\u0026eacute;sirade when landcover data were excluded from the modelling process. The Grand-Fond sector, in the center of the southern part of Grande-Terre, showed moderate uncertainty, while on Basse-Terre Island, higher uncertainty was found in intermediate elevation zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA \u0026ldquo;One at time\u0026rdquo; sensitivity analysis was used to evaluate how changes in individual input layers influenced risk predictions. The Mean Absolute Change Rate (MACR) was highest for the host density layer (MACR\u0026thinsp;=\u0026thinsp;5.73% without land cover; 6.67% with land cover data), confirming its central role in driving risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The environmental suitability of \u003cem\u003eA. variegatum\u003c/em\u003e was the second most influential predictor (MACR\u0026thinsp;=\u0026thinsp;4.05% and 4.58%), followed by the \u003cem\u003eR. microplus\u003c/em\u003e suitability (MACR\u0026thinsp;=\u0026thinsp;3.65 and 2.44%). These findings are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which summarizes the sensitivity of the MCDA risk predictions to each variable under different modelling configurations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInterestingly, the model excluding land cover data yielded better validation metrics, with an AUC of 0.70, compared to 0.65 for the land cover inclusive version (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAll inhabited islands of Guadeloupe were included in the sampling campaign. \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e ticks were detected on animals across all islands, except La D\u0026eacute;sirade, where no ticks were found. Les Saintes appeared free of \u003cem\u003eR. microplus\u003c/em\u003e. Serological evidence confirmed the circulation of \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e throughout the whole Guadeloupean archipelago, highlighting the widespread exposure of local ruminant population. It is important to note that the western part of Basse-Terre was not sampled, mainly due to the low prevalence of livestock farming in this area. This is likely a consequence of its complex topography, dominated by steep slopes and narrow valleys, which make a large-scale animal husbandry less feasible. Similarly, few animals were sampled was observed in the southeast part of Basse-Terre, a region predominantly devoted to banana cultivation. In this region, only sheep were sampled, a host less attractive to adult \u003cem\u003eA.variegatum\u003c/em\u003e adult\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, potentially leading to false absences for this species (although \u003cem\u003eR. microplus\u003c/em\u003e was detected). Furthermore, this area is also the wettest region of the island (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with environmental conditions distinct from other parts in Guadeloupe. Given the limited number of animals sampled in these regions, models may have underestimated the environmental suitability for ticks. It is also worth mentioning that tick trapping is considered inefficient for \u003cem\u003eA.variegatum\u003c/em\u003e and difficult to apply on large scale, as highlighted by Barr\u0026eacute; et al. (1997)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, who suggested that cattle represent the best \u0026ldquo;natural trap\u0026rdquo; for collecting ticks in Guadeloupe.\u003c/p\u003e\u003cp\u003eThe ENFA analysis revealed notable similarities between the ecological niches of \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e, which was expected due to the significant spatial overlap in their occurrence records. This co-occurrence is well-supported in the literature, where \u003cem\u003eRhipicephalus\u003c/em\u003e ticks are frequently found in association with \u003cem\u003eAmblyomma\u003c/em\u003e species\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Both ENFA analyses indicated high absolute marginality values for a common set of environmental variables, including temperature-related metrics, NDVI and NDMI indices, precipitation, elevation, and slope. These findings are consistent with previous studies on tropical cattle tick ecology, where similar environmental predictors have been successfully employed to study and model their ecological niches\u003csup\u003e\u003cspan additionalcitationids=\"CR75 CR76\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Among vegetation indices, NDMI consistently exhibited higher marginality values than NDVI across mean, minimum and maximum layers, suggesting that NDMI may be a more informative descriptor of moisture-related habitat conditions\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. The ENFA also contributed to refining variable selection for subsequent habitat suitability modelling\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Variance Inflation Factor analysis revealed multicollinearity between several layers, leading to the exclusion of mean values in favour of minimum and maximum values, which are more likely to represent ecological thresholds and limiting factors for tick survival and distribution as previously suggested\u003csup\u003e\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe inclusion of high-resolution land cover data did not alter the relative importance of the primary environmental variables identified by ENFA but revealed discrepancies in the environmental space occupied by each species, suggesting niche differentiation\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Among the land cover predictors, forest areas were associated with strong negative marginality, while grassland cover had positive marginality values for both species. These results reflect the origin of presence data, mostly collected in grazing and breeding areas where livestock are abundant. Both forest and grassland predictors were therefore retained for subsequent spatial modelling steps, for ecological relevance and discriminatory power.\u003c/p\u003e\u003cp\u003eThe MaxEnt models for both \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e revealed strong ecological niche overlap, with high Pearson correlation coefficients between their predicted suitability layers (0.86 without land cover, 0.93 with land cover). This similarity mirrors their co-distribution patterns and shared environmental preferences, as previously described\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. The highest suitability values for both species were predicted in Grande-Terre and Marie-Galante, areas with extensive livestock activity and favourable environmental conditions. In contrast, Basse-Terre displayed greater spatial heterogeneity. In the north, \u003cem\u003eA. variegatum\u003c/em\u003e showed high to moderate suitability, whereas \u003cem\u003eR. microplus\u003c/em\u003e was predicted with only moderate suitability. The pattern was reverse in the south, where \u003cem\u003eR. microplus\u003c/em\u003e had higher suitability. In both species, the central mountainous region of Basse-Terre, which corresponds to the national park and is characterized by dense tropical vegetation, steep elevation and an absence of ruminant hosts, consistently showed low suitability. Similarly, mangrove areas along the coast exhibited low suitability, likely due to environmental conditions that are unfavourable for tick survival and host presence.\u003c/p\u003e\u003cp\u003eAmong the environmental predictors (excluding land cover), annual precipitation emerged as the most influential variable for both tick species, with permutation importance values of 24% for \u003cem\u003eA. variegatum\u003c/em\u003e and 41% for \u003cem\u003eR. microplus\u003c/em\u003e. The critical role of rainfall in shipping tick distribution is well- documented, as it directly influences tick survival, development, and host-seeking behaviour \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. For \u003cem\u003eA. variegatum\u003c/em\u003e, suitability dropped in areas with over 2000 mm of rainfall, while for \u003cem\u003eR. microplus\u003c/em\u003e, the decline was less steep, suggesting greater tolerance to wetter conditions. These patterns correspond with previous observations\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e and may explain the higher suitability of \u003cem\u003eR. microplus\u003c/em\u003e in the southern part of Basse-Terre. However, given the low number of animals sampled in this area, additional field data are needed to validate these trends. Finally, the sharp decline in suitability observed above 3000 mm/year, for both species, likely corresponds to the high-altitude rainforest of central Basse-Terre, where ruminant hosts are absent.\u003c/p\u003e\u003cp\u003eThe maximum NDMI and the amplitude of the second harmonic of LSTN emerged as the second most influential predictors for \u003cem\u003eA. variegatum\u003c/em\u003e and \u003cem\u003eR. microplus\u003c/em\u003e, respectively. Overall, \u003cem\u003eA. variegatum\u003c/em\u003e was more influenced by moisture- and humidity-related variables, while \u003cem\u003eR. microplus\u003c/em\u003e responded primarily to temperature-related factors. By integrating the predicted suitability maps, variable importance rankings, and response curves from models excluding land cover data, several ecological inferences can be drawn. In the northeastern region of Grande-Terre, habitat suitability diverged: \u003cem\u003eA. variegatum\u003c/em\u003e showed medium suitability, while \u003cem\u003eR. microplus\u003c/em\u003e reaches high suitability values. This region, characterized by hot, dry conditions and sparse vegetation, suggests greater desiccation tolerance in \u003cem\u003eR. microplus\u003c/em\u003e. This assumptions is consistent with previous modelling efforts: Estrada-Pena, (2007)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e low environmental suitability for \u003cem\u003eA. variegatum\u003c/em\u003e in the arid northern part of Mexico, while Perez-Martinez et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e found moderate to high suitability for \u003cem\u003eR. microplus\u003c/em\u003e along dry eastern coasts in global-scale models using MaxEnt. Although spatialized relative humidity (RH) data were unavailable for Guadeloupe, despite RH being a key determinant of tropical tick survival\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, its influence may be partially captured via correlated variables like temperature and precipitation\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. NDMI, used here as proxy for environmental moisture, provided valuable information. In this context, the minimum NDMI (min_NDMI) displayed parabolic response curves for both species. \u003cem\u003eA. variegatum\u003c/em\u003e exhibited optimum suitability near min_NDMI\u0026thinsp;\u0026asymp;\u0026thinsp;0.2, while \u003cem\u003eR. microplus\u003c/em\u003e favored drier conditions, with an optimum near NDMI\u0026thinsp;\u0026asymp;\u0026thinsp;0, reinforcing its apparent greater resilience to aridity. In contrast, high NDMI values (both min and max layer) were associated with low predicted suitability for both species, conditions found in the forested, mountainous areas of central Basse-Terre, where livestock farming is prohibited. However, it is important to acknowledge the limits of macroscale models, as microhabitat-scale humidity conditions (e.g., shaded ground, dense vegetation cover, soil cracks) may provide suitable environments for tick survival, that are not captured at this spatial resolution\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSlope was the third most important predictor for both species (permutation importance: 19% for \u003cem\u003eA. variegatum\u003c/em\u003e, 15% for \u003cem\u003eR. microplus\u003c/em\u003e). The response curves followed a similar pattern: both species displayed highest suitability at low to moderate slope values, declining at both extremes. Steep slopes may restrict livestock presence due to accessibility issues, while flat areas such as mangroves or sugarcane plantations, often unsuitable for livestock, were largely devoid of tick records\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Among temperature-related variables, minimum LSTN (min_LSTN) was the most influential predictor for \u003cem\u003eA. variegatum\u003c/em\u003e (9%), while maximum daytime LST (max_LSTD) was most important for \u003cem\u003eR. microplus\u003c/em\u003e (9%). All temperature predictors displayed decreasing response curves, indicating a preference for cooler environments. High temperatures likely increase desiccation risk, limiting tick survival. Notably, the max_LSTD response curve for \u003cem\u003eA. variegatum\u003c/em\u003e declined linearly with increasing temperature, while for \u003cem\u003eR. microplus\u003c/em\u003e, it remains stable up to 305\u0026deg;K, before sharply decreasing, suggesting a greater heat tolerance in this species.\u003c/p\u003e\u003cp\u003eIncluding land cover data significantly altered predicted suitability maps and response curves, for both species, largely constraining areas of high suitability to grassland regions. This pattern was mainly driven by the \"Grass\" class, which had the highest permutation importance and strongly increasing response curves. Although this may suggest a potential model overfitting, since most presence points were in livestock-utilized grasslands\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e, it remains biologically justified. In Guadeloupe, domestic ruminants are the primary hosts of both tick species\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, with wildlife infestations rare and generally incidental\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Therefore, high-resolution land cover data improves both the ecological realism and spatial precision of the models, as supported by improved validation metrics (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eR. microplus\u003c/em\u003e appears to occupy a broader ecological niche than \u003cem\u003eA. variegatum\u003c/em\u003e in Guadeloupe, which is consistent with its wider global distribution. A factor that could explain this higher resistance to climatic variations could be the shorter free-living life of monoxenic \u003cem\u003eR. microplus\u003c/em\u003e and egg incubation period if compared to three hosts ticks \u003cem\u003eAmblyomma\u0026rsquo;s\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. While \u003cem\u003eA. variegatum\u003c/em\u003e is typically confined to tropical climates\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eR. microplus\u003c/em\u003e has been reported across a more diverse range of environments worldwide\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Although \u003cem\u003eR. microplus\u003c/em\u003e may not find optimal conditions throughout the entire Guadeloupean territory, the highest predicted suitability values (\u0026gt;\u0026thinsp;0.9) were concentrated in the eastern part of Marie-Galante. In contrast, \u003cem\u003eA. variegatum\u003c/em\u003e exhibited very high suitability (\u0026gt;\u0026thinsp;0.9) across several areas, including Marie-Galante, Grande-Terre, and parts of Basse-Terre. These findings are consistent with field observations, where \u003cem\u003eR. microplus\u003c/em\u003e infestations are less frequent than \u003cem\u003eA. variegatum\u003c/em\u003e. The present models provide the first spatially explicit maps of the ecological niches of the two tick species representing a great threat in Guadeloupe\u0026rsquo;s livestock production system.\u003c/p\u003e\u003cp\u003eThe Multi-Criteria Decision Analysis (MCDA) approach was effectively employed to model the spatial risk of \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e infection across the Guadeloupean archipelago. Models using tick suitability layers without land cover achieved an AUC of 0.702, while models including land cover had a lower AUC of 0.654, possibly reflecting overfitting to grassland areas. Although internal MaxEnt metrics improved with land cover, these models may offer less insight into actual disease dynamics. This suggests that simpler models may better reflect ecological realities in tick-borne disease modeling.\u003c/p\u003e\u003cp\u003eThese AUC values indicate moderate predictive performance\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. However, validation procedure involved challenges. Infection status was assessed using the MAP1B serological test, which, though specific\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e, may underperform under endemic conditions\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Since MAP1B seropositivity can decline in frequently exposed animals\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e, a conservative validation approach was adopted: pixels were labeled negative only if at least three animals tested negative, in accordance with OIE Terrestrial Manual recommendations\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccording to the literature on MCDA validation, more direct indicators of disease circulation, such as clinical case reports or molecular detection, would provide stronger support for risk model validation\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Unfortunately, such data are lacking in Guadeloupe due to limited surveillance of tick-borne diseases in livestock sector. Strengthening surveillance system for both tick infestation and tick borne diseases appears essential, especially in light of the vulnerability of the region to diseases\u0026rsquo; spread across the Caribbean\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. For example, during the winter of 2025, the Les Saintes islands, historically free of heartwater\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e, experienced several heartwater outbreaks (Giles Manuel, veterinary, personal communication). Our recent sampling confirmed serological evidences of infection in goats on the island \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e indicating an active risk of disease dissemination.\u003c/p\u003e\u003cp\u003eDespite limitations, the overall methodology appears promising for assessing the risk and spatial distribution of tick-borne diseases like heartwater, especially under data-scarce conditions. Incorporating targeted epidemiological variables could further improve the accuracy of risk predictions. For example, Haoran et al. (2021)\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e demonstrated that proximity to recent clinical cases can serve as a valuable spatial predictor, while serological data on host protection could refine spatial estimates\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. However, mapping host resistance, shaped by multiple factors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, requires extensive field data and robust surveillance. The resulting maps provided a meaningful proxy for heartwater disease which could be extended to other tick-borne disease risks in Guadeloupe due to the broad relation considered between the disease and the different risk layers used in the MCDA. To our knowledge, this constitutes the first spatially explicit risk assessment of \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e in the region.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first spatially explicit assessment of ecological suitability for \u003cem\u003eAmblyomma variegatum\u003c/em\u003e and \u003cem\u003eRhipicephalus microplus\u003c/em\u003e, along with the first effort to map heartwater risk in Guadeloupe. By combining environmental predictors and species distribution models within a multi-criteria decision analysis framework, we produced informative maps that highlight key areas of vector suitability and potential disease risk. Despite limitations, particularly in sampling coverage and the absence of disease-specific indicators, our approach aligns with existing ecological knowledge and field observations.\u003c/p\u003e\u003cp\u003eThe resulting maps offer a valuable operational tool for local stakeholders, enabling prioritization of field investigations and guiding surveillance efforts. Notably, ecologically distinct zones such as southern Basse-Terre warrant further exploration due to their environmental heterogeneity and potential under-sampling. Future risk assessments could benefit from the integration of serological, molecular, or clinical data to refine model accuracy and resolution. Expanding such efforts would not only enhance our understanding of disease dynamics but also support more targeted and effective control strategies.\u003c/p\u003e\u003cp\u003eBeyond the Guadeloupean context, the methodological framework proposed here is transferable to other Caribbean islands or tropical regions facing similar data constraints. It offers a scalable and practical approach to support surveillance planning and strengthen animal health systems in vulnerable territories affected by tick-borne diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eTick presence data are not publicy available due to confidentiality concerns, as they were collected from livestock and include geolocated information obtained with the consent of individual farmers. Requests for access to these data can be considered on a case-by-case basis by the corresponding author, subject to appropriate data-sharing agreements.\u003c/p\u003e\n\u003cp\u003eAll environmental raster datasets used in the modelling process are publicy available from the following sources:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTopographic data: BD-TOPO, IGN - https://geoservices.ign.fr/bdtopo\u003c/li\u003e\n \u003cli\u003eMeteorological data: M\u0026eacute;t\u0026eacute;o France - https://meteo.data.gouv.fr/datasets/donnees-climatologiques-de-base-quotidiennes/\u003c/li\u003e\n \u003cli\u003eMODIS imagery: MODIStsp R package - https://github.com/ropensci/MODIStsp\u003c/li\u003e\n \u003cli\u003eHigh-resolution landcover data : Karucover 2022 - https://catalogue.karugeo.fr/geonetwork/srv/fre/catalog.search#/metadata/ee390aa5-9eb8-4f9f-94da-6833d25662a\u003c/li\u003e\n\u003c/ul\u003e\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank all participants of the TISARU project, from farmers to scientific experts, for their valuable contributions. We are especially grateful to the SANIGWA association for their assistance in collecting tick presence and cattle serology data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contribution\u003c/p\u003e\n\u003cp\u003eLG; EE: Conceptualization/design, investigation, formal analysis, methodology, writing, writing \u0026plusmn; review, validation, data curation, supervision.\u003c/p\u003e\n\u003cp\u003eVD : Conducted field surveys, formal analysis, software, drafting the initial manuscript, validation.\u003c/p\u003e\n\u003cp\u003eAll authors corrected and approved the submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp id=\"_Toc99629453\"\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the United States Department of Agriculture (USDA) under grant number 58-3022-1-018-F (Risk of Arthropod-borne diseases in the Caribbean; RACE). The authors also acknowledge the support of the Guadeloupe region and\u0026nbsp;European Agricultural Fund for Rural Development (EAFRD) through the\u0026nbsp;TISARU project (FEADER_M16_2021_01), which\u0026nbsp;provided logistical support for animal sampling activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eTick presence and cattle serological data were collected as part of survey conducted under the RACE and TISARU projects. The study protocol was reviewed and approved by the relevant ethical authority, under APAFIS approval number #43250-2023050210511531 v3, prior to implementation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllsopp, B. A. Heartwater \u0026ndash; Ehrlichia ruminantium infection: -EN- -FR- La cowdriose \u0026ndash; Infection par Ehrlichia ruminantium -ES- Cowdriosis \u0026ndash; Infecci\u0026oacute;n por Ehrlichia ruminantium. \u003cem\u003eRev. Sci. Tech. 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GIS-based approach for mapping the density and distribution of crossbred cattle. \u003cem\u003eIndian J. Anim. Sci\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7142010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7142010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeartwater is a tick-borne disease affecting livestock in Africa and the Caribbean, including Guadeloupe, where it threatens animal health and productivity. While \u003cem\u003eAmblyomma variegatum\u003c/em\u003e has long been recognized as the primary vector, recent studies suggest \u003cem\u003eRhipicephalus microplus\u003c/em\u003e may also transmit \u003cem\u003eEhrlichia ruminantium\u003c/em\u003e, the causative agent. This study presents a spatial modelling framework to assess heartwater risk across Guadeloupe. Tick presence data collected during livestock inspections were combined with environmental variables derived from satellite imagery and other geospatial sources. Ecological Niche Factor Analysis identified key environmental predictors, which were then used to build MaxEnt models and generate suitability maps for both tick species. These maps revealed distinct ecological preferences and were integrated with cattle density data using a Multi-Criteria Decision Analysis approach, with expert-derived weighting, to produce a composite risk index. The resulting maps provide the first spatially explicit assessment of heartwater risk in Guadeloupe. This approach offers a reproducible method for mapping tick-borne disease risk in data-limited tropical regions and can guide targeted surveillance and control strategies.\u003c/p\u003e","manuscriptTitle":"Mapping heartwater risk in Guadeloupe: a combination of spatial modelling approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 19:04:38","doi":"10.21203/rs.3.rs-7142010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-06T20:16:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T18:43:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T14:31:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85297450471921446378345209309751011111","date":"2025-08-28T13:17:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330169955898106351999038706329024268792","date":"2025-08-26T22:57:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-26T20:00:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T19:53:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-26T19:21:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T12:34:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-08T12:30:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2c8861a8-d2f1-4696-ab1e-b2739a5a0768","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53848331,"name":"Biological sciences/Ecology"},{"id":53848332,"name":"Earth and environmental sciences/Ecology"},{"id":53848333,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-12-08T16:11:42+00:00","versionOfRecord":{"articleIdentity":"rs-7142010","link":"https://doi.org/10.1038/s41598-025-30181-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-01 15:57:56","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-09-03 19:04:38","video":"","vorDoi":"10.1038/s41598-025-30181-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30181-4","workflowStages":[]},"version":"v1","identity":"rs-7142010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7142010","identity":"rs-7142010","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}