Crossing through danger: Spatial drivers of roadkill risk for felids in the fragmented Middle Magdalena valley

Crossing through danger: Spatial drivers of roadkill risk for felids in the fragmented Middle Magdalena valley | 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 Research Article Crossing through danger: Spatial drivers of roadkill risk for felids in the fragmented Middle Magdalena valley Julián Arango-Lozano, Karime Angarita-Corzo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7198781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Middle Magdalena valley in Colombia shows great fragmentation processes, which threatens conservation values and may particularly impact key ecological species such as wide-ranging felids. This study evaluates the vulnerability of the five native felid species ( Herpailurus yagouaroundi, Leopardus pardalis , L. wiedii, Panthera onca and Puma concolor ) to roadkill along a 250 km stretch of the Ruta del Sol highway. We combined roadkill data with presence records from field surveys, camera traps and citizen science platforms between 2018 and 2025. Spatial analyses included kernel density estimation of roadkill hotspots and a friction surface model to quantify landscape permeability. A beta regression model assessed the relationship between roadkill risk, proximity to hotspots, landscape resistance, and species identity. We detected four roadkill hotspots associated with a lower landscape resistance, particularly around Puerto Parra and Puerto Boyacá. P. concolor showed a significantly lower risk of roadkill compared to H. yagouaroundi , while other species showed similar risk levels. More than 65% of the landscape was classified as highly resistant to movement, indicating substantial fragmentation. Notably, roadkill hotspots overlapped with permeable habitat corridors, creating a conservation paradox where areas suitable for wildlife movement are also the most dangerous. We highlight the urgent need for spatially explicit mitigation measures, including wildlife crossings with guiding fences and habitat restoration in key corridors. As the convergence of five felid species suggests potential for interspecific competition and cumulative impacts, targeted conservation efforts are essential to maintain ecosystem integrity. Conservation planning Cougar Habitat permeability Jaguar Landscape fragmentation Ocelot Spatial modeling Road ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Road infrastructure is an important topic of human development but often comes at a significant ecological cost (Benítez-López et al. 2010 ; Bennett 2017 ). Roads, highways, fragment habitats, alter animal behavior, and create barriers which can lead to population isolation (Forman and Alexander 1998 ; Shepard et al. 2008 ). Among other, wildlife roadkill is the most direct and visible impact to biodiversity (Coffin 2007 ; Rendall et al. 2021 ), which has been documented as a leading cause of mortality for various species globally (Pinto et al. 2020 ; Grilo et al, 2021 ). Furthermore, the way the roads are designed, including their geometry and placement, can exacerbate this issue, making certain areas more hazardous for animals attempting to cross (de Freitas et al. 2014 ; Arango-Lozano and Patiño-Siro 2020 ). In regions with high biodiversity, such as Colombia, the consequences of roadkill can be particularly severe, leading to the decline of already vulnerable species (Quintero-Ángel et al. 2012 ; Meza et al. 2019 ; Medrano-Vizcaíno et al. 2022 ). Habitat fragmentation, increased by infrastructure development, leads wildlife to move through increasingly restricted and degraded habitats (Reid et al. 2004 ; Coffin 2007 ; Di Giulio et al. 2009 ). Important predators like Felids, which require large territories, are particularly susceptible to roads posed dangers when crossing fragmented landscapes (Crooks et al. 2011 ; Zanin et al. 2015 ; Blackburn et al. 2021 ; Quintana et al. 2022 ). Fragmentation not only forces these animals closer to roadways but also increases their exposure to additional stressors such as noise, pollution, and human activity, which further elevate their risk of roadkill (Forman and Alexander 1998 ; Benítez-López et al. 2010 ; Bennett 2017 ). In Colombia, the Middle Magdalena Valley region is an important reservoir of tropical forest (Myers 1988 ; Meza et al. 2019 ; Ovalle-Pacheco et al. 2019 ), home to several protected areas, including the Serranía de los Yariguíes, Reserva Forestal del Magdalena, Serranía de las Quinchas, and the Humedal San Silvestre (Donegan et al. 2010 ; Ovalle-Pacheco et al. 2019 ; Gaona 2025 ). These areas are crucial for the conservation of a rich diversity of mammalian species, particularly felids like Leopardus pardalis , L. wiedii , Herpailurus yagouaroundi , Puma concolor , and Panthera onca , species with a crucial role in maintaining ecosystem functionality when controlling prey populations, which helps regulate the balance within trophic levels (Figel et al. 2019 ; Boron et al. 2020 ; Machado-Aguilera et al. 2024 ;). However, the region is also a focal point for extensive industrial and infrastructural development, including projects by fossil energy, various solar photovoltaic parks, and ongoing highway construction (Meza et al. 2019 ; Boron et al. 2020 ; Figel et al. 2021 ; Meza 2023). The constant flux of heavy trucks on major roads, such as the Ruta del Sol highway in national routes (RN) 4510, 4511, 4513, exacerbates the risk of roadkill for these wide-ranging species (Diaz-Pulido and Benítez 2013 ; Meza et al. 2019 ; Meza 2023). As consequences, different felids have been detected roadkilled in the ruta del sol highway (9 individuals of L. pardalis , 4 of H. yagouaroundi and 1 of P. concolor; Fig. 1 ), and others have been near detected to the road but not roadkilled ( L. wiedii , P. onca ; Fig. 1 ). Felids in the known fragmented region of the Magdalena Medio require larger conserved zones compared to other mammals to maintain their biological cycles (Boron et al. 2020 ; Figel et al. 2021 ). Panthera onca , has been recognized by international conservation efforts with the creation of corridors as part of broader conservation goals (Machado-Aguilera et al. 2024 ). Due to their essential role as keystone species, Felids conservation is not just about protecting individual species but about safeguarding the entire ecosystem they inhabit (Meza et al. 2019 ; Figel et al. 2021 ; Meza 2023). Special attention must be given to these species, as their decline can lead to cascading effects that disrupt biodiversity and ecosystem processes (Blackburn et al. 2021 ; Quintana et al. 2022 ). The combination of habitat fragmentation and road infrastructure development in the biodiversity-rich Middle Magdalena region presents a significant threat to felid conservation (Meza et al. 2019 ; Blackburn et al. 2021 ; Figel et al. 2021 ; Quintana et al. 2022 Meza 2023). Therefore, the objective of this research was to identify the most vulnerable areas for felids to be probable roadkilled within the fragmented landscape of green corridors in the Magdalena Medio region, focusing specifically on the Ruta del Sol highway. The study concentrated on the stretch between the intersections with Puerto Salgar (Cundinamarca) and Barrancabermeja (Santander) in Colombia's inter-Andean valleys, using records of both live sightings and roadkill incidents involving felid species. Materials and Methods Study area We conducted the study in Colombia’s Middle Magdalena Valley region, focusing on areas along and surrounding national routes 4510, 4511, and 4513; collectively known as part of the Ruta del Sol (Fig. 2 ), specifically between the municipalities of Puerto Salgar (Cundinamarca) and Barrancabermeja (Santander). The region consists of a mosaic of lowland alluvial floodplains and remnants of tropical moist forest (Myers 1988 ; Meza et al. 2019 ; Gaona et al; 2025). The landscape is highly fragmented due to extensive cattle and agricultural activities such as oil palm plantations; furthermore, intense anthropogenic pressure from infrastructure and energy projects, including oil extraction, the development of solar photovoltaic parks, and the recent expansion of highways of which the Ruta del Sol is a central component (Meza 2023; Montes-Rojas et al. 2024 ; Gaona et al; 2025). Climate with high annual rainfall concentrated in two main rainy seasons (typically between April - May and September–November), and average temperatures ranging from 26°C to 30°C (Jaimes et al. 2016 ; Montes-Rojas et al. 2024 ; Gaona et al; 2025). Roadkill hotspots From December 2022 to March 2025, we collected 396 roadkill events of vertebrates in the ruta del sol highway (Puerto Salgar – Barrancabermeja extension). The records were collected with a periodic survey of 2 times per week one person in a car at 70 km/h searching for carcasses. However, given that the highway spans more than 250 km, we also incorporated data provided by residents who submitted photographs of roadkilled animals along with precise geographic coordinates. When possible, for verification purposes, we asked contributors to specify the exact kilometer marker point (PK) which also is indicated along the roadside every kilometer. We only included roadkill records based on our own direct observations and reports from residents that included clearly identifiable carcasses’ photographs and locations. With the known roadkill data, we classified the records between taxonomic and date encounters. To identify roadkill hotspots, we applied the kernel density analysis using the online version of ArcGis (Esri, 2025 ). Rather than relying on specialized software such as Siriema 2.0 (Coelho et al. 2014 ), which often emphasizes detailed and site-specific modeling, we aimed to produce a broader, more generalized spatial output for the identification of areas with a higher concentration of roadkill events along the highway based on point density patterns; since Kernel density estimation (KDE) calculates the density of point features in a neighborhood around each output raster cell, creating a surface of intensity values (Silverman, 1986; Medinas et al. 2021 ). To reflect a relative likelihood of roadkill occurrence across the study area, we rescaled the values of the resulted raster between 0 and 1, representing a continuous probability surface; it is from lower to higher probability zones. This generalization was intentional, as it enabled the integration of roadkill risk into wider geographic space. Landscape permeability (friction) To assess landscape permeability for felid species in the nearby region: “ Leopardus pardalis , L. wiedii , Herpailurus yagouaroundi , Puma concolor , and Panthera onca ”, there was conducted a friction analysis using ArcGIS Online (Esri, 2025 ), combining multiple spatial layers into a single raster surface quantifying the resistance to movement (Gietl et al. 2008 ; Porter et al. 2015 ). Each input layer represented a key ecological or anthropogenic factor influencing felid mobility: land cover types (Lc), proximity to core areas (Ca), distance between habitat fragments (Df), and hydrological features (Hr). Each category within these variables was assigned a resistance value ranging from 1 (low difficulty of movement) to 5 (high difficulty), based on the constructed agreement suitability criteria for felids in our study such as: habitat preference to Primary forests, low fragmentation tolerance, and high sensitivity to human disturbance, with a 40 km 2 minimum patch for inhabiting (Zanin et al. 2015 ; Figel et al. 2019 , 2021 ; Meza et al. 2019 ; Quintana et al. 2022 ; Machado-Aguilera et al. 2024 ). For instance, dense riparian forests and gallery woodlands were assigned the lowest resistance (1), while urban areas and large-scale agricultural zones received the highest (5), see Table 1 . These reclassified layers were then weighted and summed to produce a composite friction surface, where lower values represent areas of higher habitat suitability and connectivity, and higher values highlight regions of greater movement difficulty or fragmentation. The extension of the analysis occupies a 100 km buffer from the middle of the highway. All analyses were performed in R (R Core Team 2025 ). Table 1 Description of spatial layers and associated friction values used in the friction -connectivity analysis. Each category within the layers was assigned a friction value from 1 (low resistance) to 5 (high resistance), representing its influence on the agreement felid species movement across the landscape. Land Cover Types - Lc Category Description Friction Value Preferred habitat Gallery and/or riparian forest, Dense high floodplain forest, Palm groves 1 Semi-natural cover Sandbanks, Tall secondary vegetation, Low secondary vegetation 2 Moderately altered cover Herbaceous vegetation (various types), Oil palm plantations, Forest plantations, Rivers (50 m buffer), Swamp areas 3 Artificial water/pasture areas Artificial water bodies, Aquaculture ponds, Oxidation lagoons, Clean pastures 4 Highly transformed areas Intensive agricultural areas, Roads, Urban and industrial areas, Degraded or burned zones 5 Core Areas - Ca Category Description Friction Value Inside core areas inside the core zones (habitat patches of > 40 km2 for our felids agreement) 1 Outside core areas outside the core zones (habitat patches of > 40 km2 for our felids agreement) 5 Distance to Preferred Habitat Fragments - Df Distance Range Friction Value < 250 m 1 250 m – < 500 m 2 500 m – < 750 m 3 750 m – 1000 m 5 Water Resource Type (hydric resource - Hr) Category Description Friction Value Natural water bodies Natural lagoons, lakes, and swamps, rivers (50 m) 1 Semi-natural wetlands Swampy areas, aquatic vegetation, natural sandy areas 2 Artificial water bodies Artificial water bodies, aquaculture ponds 3 Altered water systems Oxidation lagoons, canals, peat bogs, glacial and nival zones 4 Non-related covers Coverages unrelated to the water resource 5 Note: All layers in this analysis were downloaded from IDEAM ( https://experience.arcgis.com/experience/568ddab184334f6b81a04d2fe9aac262/page/Datos-Abiertos-Geogr%C3%A1ficos-/ ), at a resolution 1:100000 from the year 2022. Roadkill and landscape permeability risk To detect whether felid species occurring near the Ruta del Sol highway are at greater risk of roadkill, we developed a risk model based on the spatial occurrence of these species in relation to key landscape variables. The methodological approach included the following steps: Data collection of species occurrences . A comprehensive database was built using Presence records of felid species obtained from public platforms such as iNaturalist, covering a time frame from 2018 to 2025. Each record was reviewed to ensure spatial accuracy and appropriate use; and Roadkill records, collected through systematic field surveys conducted between 2022 and 2025 along different sections of the Ruta del Sol . In total we recorded 15 roadkill events as follows: 9 of Leopardus pardalis , 5 of Herpailurus yagouaroundi and 1 of Puma concolor (Table 2 , Fig. 2 ) by surveying and 169 observation events from opportunistic encounters (camera trap) and iNaturalist records (mainly by camera traps) in all the 5 recognized felid species in the Middle Magdalena Valley ( Supplementary material 2 https://osf.io/94saf/ ). Table 2 Description of the roadkilled felid species in the ruta del sol Highway. PK = Kilometer marker point, NR = National route. Date Species PK NR Longitude Latitude 19/06/2023 Leopardus pardalis 79 + 100 4510 -74,488075 6,260016 27/09/2023 Leopardus pardalis 22 + 100 4511 -74,642408 5,482914 23/10/2023 Herpailurus yagouaroundi 133 + 580 4510 -73,658974 7,045297 24/12/2023 Leopardus pardalis 23 + 600 4511 -73,871522 6,743528 13/01/2024 Leopardus pardalis 33 + 300 4511 -73,930012 6,666792 14/01/2024 Leopardus pardalis 91 + 140 4511 -73,930012 6,666792 26/01/2024 Leopardus pardalis 121 + 400 4510 -74,101242 6,521126 1/03/2024 Herpailurus yagouaroundi 134 + 100 4511 -74,256577 6,478132 23/05/2024 Herpailurus yagouaroundi 125 + 900 4510 -74,561862 6,184405 27/05/2024 Leopardus pardalis 113 + 000 4510 -74,553307 6,096157 14/06/2024 Leopardus pardalis 33 + 900 4511 -74,551989 6,072828 26/08/2024 Puma concolor 108 + 000 4510 -74,622717 5,722991 9/11/2024 Leopardus pardalis 104 + 700 4511 -73,82105 6,778662 1/03/2025 Herpailurus yagouaroundi 134 + 000 4511 -73,71403 6,938518 22/04/2025 Herpailurus yagouaroundi 140 + 000 4511 -73,686071 6,998461 Construction of a Spatial Occurrence Matrix . Using both presence and roadkill data, we created a spatial occurrence matrix that allowed us to identify areas where species are likely to occur. This matrix was georeferenced and overlaid with Distance to roadkill hotspots (areas of high roadkill density identified through kernel density analysis); and the landscape friction raster output, the continuous surface representing movement resistance across the landscape, where higher values indicate lower permeability for wildlife. All variables (distance to hotspot and landscape friction) were standardized (mean = 0, standard deviation = 1) to facilitate interpretation of model coefficients and avoid scaling issues. Statistical Modeling of Risk . We used a beta regression model (via the betareg function in R, Zeileis et al. 2016) with a logit link, appropriate for modeling proportional response variables constrained between 0 and 1. The response variable was the adjusted risk probability of a species occurring in areas with both high roadkill potential and varying levels of landscape friction. The predictors included: distance to hotspot; normalized landscape friction, species identity as a categorical variable, with Herpailurus yagouaroundi as the reference category. With the logit transformation the model formula is: $$\:logit\left({y}_{i}\right)={\beta\:}_{0}+{\beta\:}_{1}\cdot\:{x}_{1i}+{\beta\:}_{2}\cdot\:{x}_{2i}+{\sum\:}_{j=1}^{k}{\gamma\:}_{j}\cdot\:Species{j}_{i}$$ Where: \(\:{y}_{i}\) is the risk probability (adjusted 0–1) for observation i . \(\:logit\left({y}_{i}\right)=log\left(\frac{y1}{1-yi}\right)\) , is the logit transformation. \(\:{x}_{1i}\) ​ is the standardized distance to hotspot. \(\:{x}_{2i}\) is the standardized landscape friction. \(\:{Species}_{i}\) are dummy variables for species identity (reference: H. yagouaroundi ) β 0 ​ = Intercept (risk for the reference species at average conditions), β 1​ = Effect of distance to hotspot, β 2 ​ = Effect of landscape friction, γ j = Coefficients for each species jjj , comparing them to the reference species. Note This expression captures how each predictor (distance, friction, and species) influences the log-odds of the species being at risk in the landscape. All analyses were performed in R (R Core Team 2025 ). Results Roadkill hotspots Out of the kernel density analysis, it was found 4 mainly sections in the road with a greater spatial aggregation of roadkills located in the middle of the route near to the localities of Puerto Boyacá (Boyacá), Puerto Araujo (Antioquia), Cimitarra (Santander), and the most important near to Puerto Parra (Santander); the hotspots are depicted in Fig. 3 a. The regions were involved with greater accumulation of the species: Tamandua Mexicana with 20% of the total roadkills (396), Procyon cancrivorus and Cerdocyon thous each with 10% of the roadkills; Hydrochoerus isthmius with 6.3% of the roadkills, and Caiman crocodilus with 5.7% of the roadkills; the full description of the roadkill information can be accessed in Supplementary Material 1 ( https://osf.io/94saf/ ). Landscape permeability (friction) The friction results showed more than 65% of the geographical space evaluated as with high very high resistance (> 4.5) it is spaces with agricultural/ urban areas; less than 20% of the geographical space as with very low friction (< 1), it is gallery/riparian forests; and les than 15% of the total space was represented by low (1–2), moderate (2–3) and moderate to high (3-4.5). So, it is recognized that the landscape surrounding the road is predominantly composed of areas with very high movement resistance, indicating low permeability and significant fragmentation (Fig. 3 b). Roadkill and landscape permeability risk When generated the model of mixing effects of landscape permeability + distance to hotspots (i.e., roadkill risk), we found a strong positive effect of distance to hotspot (β = 1.304, p < 0.001), suggesting the expected results that individuals located closer from hotspots experience a higher predicted risk. A significant negative effect of landscape friction (β = -0.777, p < 0.001), indicating that areas with higher resistance to movement are associated with lower risk probabilities. Among the species-level predictors (with reference level: Herpailurus yagouaroundi ), only Puma concolor exhibited a significantly lower risk probability (β = -0.414, p = 0.016). The remaining species ( Leopadus wiedii , L. pardalis , Panthera onca ) did not differ significantly from the reference species in terms of predicted risk (Table 3 ). Additionally, the precision parameter (phi) was high and significant (φ = 23.25, p < 0.001), indicating limited dispersion in the risk predictions and increased confidence in the estimates. Table 3 Summary of fixed effects from the beta regression model predicting felid roadkill risk based on landscape friction, distance to hotspots, and species identity. Reference species for analysis = Herpailurus yagouaroundi ) Predictor Estimate Std. Error z value p-value Significance (Intercept) -1.220 0.116 -10.540 < 0.001 *** Distance to hotspot 1.304 0.048 27.066 < 0.001 *** Landscape friction -0.777 0.042 -18.405 < 0.001 *** Leopardus wiedii -0.198 0.157 -1.263 0.207 Leopardus pardalis -0.101 0.131 -0.774 0.439 Panthera onca -0.114 0.132 -0.862 0.389 Puma concolor -0.414 0.172 -2.410 0.016 * Note: Intercept = Distance to hotspot + Landscape friction; Significance codes: *** p < 0.001, ** p < 0.01, * p < 0.05 Discussion The fragmentation processes in the Middle Magdalena Valley have long been recognized as a major driver of disrupted ecological connectivity among various animal species (Boron et al. 2020 ; Meza et al. 2019 ; Montes-Rojas et al. 2024 ). Where large felids such as the jaguar ( Panthera onca ) are of particular concern due to their critical ecological roles (Figel et al. 2019 ; Figel et al. 2021 ; Machado-Aguilera et al. 2024 ). Despite significant international conservation efforts (Rabinowitz and Zeller 2010 ; Machado-Aguilera et al. 2024 ), multiple studies have emphasized the ongoing vulnerability not only for jaguars but the four other felid species inhabiting the region, all of which require vast, continuous territories to fulfill their functional roles within ecosystems (Figel et al. 2019 ; Boron et al. 2020 ; Figel et al. 2021 ; Meza et al. 2023). In highly fragmented landscapes, these felids may travel even greater distances to find essential resources, thereby increasing their exposure to anthropogenic threats such as the shown in this research, the roadkill and human-wildlife conflict (Zanin et al. 2015 ; Figel et al. 2021 ; Quintana et al. 2022 ; Machado-Aguilera et al. 2024 ). Even with fragmented habitats, the Middle Magdalena may not sustain felids without their critical resources, among others, prey species (Zanin et al. 2015 ; Wolf and Ripple 2016 ). Although identified through roadkill records, species such as Tamandua mexicana , Hydrochoerus isthmius , Procyon cancrivorus , Cerdocyon thous , and Caiman crocodilus , which together account for more than 50% of all recorded roadkill events (see Supplementary Material 1 , https://osf.io/94saf/ ), highlighting the ecological significance of the region, which serves as a key refuge for maintaining overall biodiversity and supporting the functioning of surrounding ecosystems, particularly for top predators as felids (Myers 1988 ; Figel et al. 2019 ; Boron et al. 2020 ; Quintana et al. 2022 ; Montes-Rojas et al. 2024 ). Our spatial analyses revealed that risk for felids increases near identified roadkill hotspots, and decreases in areas characterized by higher landscape friction, interpreted here as more fragmented or less permeable landscapes (Fig. 3 ). This pattern suggests that felids preferentially move through more permeable and better-connected habitats, which unfortunately tend to coincide with areas of higher roadkill probability (Fig. 4 ); this is paradox but a recognized pattern in road ecology, where animals are more likely to cross the roads in areas that still provide ecological functionality (Coffin 2007 ; Crooks et al. 2011 ; Bennet 2017; Blackburn et al. 2021 ; Meza et al. 2023).​ While in general the tendencies to risk are similar in all felid species, Our beta regression model indicates that Puma concolor exhibits a significantly lower predicted risk of roadkill compared to Herpailurus yagouaroundi (reference species for the analysis), which may reflect behavioral adaptations such as greater avoidance of roads or preference for larger forested areas (Dickson et al. 2005 ; lewis et al. 2015 ; Wang et al. 2017 ; Figel et al. 2021 ; Carvalho et al. 2024 ). In contrast, species like Leopardus pardalis and Herpailurus yagouaroundi with expected avoiding modified habitats (Fischer et al. 2021 ; Bolze et al. 2023 ) have been detected occupying disturbed environments (Dotta and Verdade 2009 ; Giordano 2016 ; Coronado-Quibrera et al. 2019 ; Fornitano et al. 2024 ); and in our records, the felids with more roadkill occurrences. In the other hand, although Leopardus wiedii and Panthera onca were not detected as roadkill victims, their presence near roads suggests potential risk, possibly mitigated by their elusive nature and preference for well-preserved habitats (Hodge 2014 ; Machado-Aguilera et al. 2024 ). However, our observations to these last species recognize them in modified lands as oil palm crops, just as other studies have shown (Boron et al. 2016 ; Mendes-Oliveira et al. 2017 ; Boron et al. 2020 ). The conservation of apex predators such as Puma concolor and Panthera onca is particularly crucial, their presence maintains ecosystem balance through regulation of prey populations and competitive interactions (Wang et al. 2017 ; Figel et al. 2021 ; Machado-Aguilera et al. 2024 ). The loss or decline of these species could trigger trophic cascades, leading to overpopulation of certain herbivores or mesopredators and further degradation of already fragile habitats (Paviolo et al. 2016 ; Wolf and Ripple 2016 ; Ordiz et al. 2021 ; Quintana et al. 2022 ). In the Middle Magdalena Valley, biodiversity remains high but increasingly vulnerable, where the persistence of large felids contributes to maintaining the ecological processes and biodiversity functionality (Figel et al. 2019 , 2021 ; Machado-Aguilera et al. 2024 ). It is important to highlight that convergence of five felid species within the same fragmented landscape raises important ecological questions regarding resource competition and habitat carrying capacity (Figel et al. 2019 ; Boron et al. 2020 ). While current prey availability, as evidenced by roadkill records, seem matching for multiple of our felids, the long-term sustainability of these communities remains uncertain under ongoing habitat loss and fragmentation (Zanin et al. 2015 ; Wolf and Ripple 2016 ). The high number of roadkills observed for species like Herpailurus yagouaroundi (5 individuals) and Leopardus pardalis (9 individuals) is alarming given the typically low population densities of felids and their slow reproductive rates (Wang et al. 2017 ; Figel et al. 2019 ; Quintana et al. 2022 ). Although these numbers may seem modest, even a few deaths can have significant demographic impacts on local populations, particularly for these wide-ranging carnivores (Paviolo et al. 2016 ; Ordiz et al. 2021 ; Quintana et al. 2022 ). The potential future detection of roadkilled individuals of larger and more endangered species, such as the jaguar, would signal an even greater conservation crisis, highlighting the urgency for immediate and effective action (Rabinowitz and Zeller 2022; Machado-Aguilera et al. 2024 ). While conservation strategies are already being implemented across the Middle Magdalena region, including habitat restoration projects, the creation of private reserves, and the promotion of wildlife-friendly practices in agricultural landscapes (Rabinowitz and Zeller 2010 ; Boron et al. 2016 ; Figel et al. 2019 ; Machado-Aguilera et al. 2024 ; Montes-Rojas et al. 2024 ); their success will depend on their ability to incorporate dynamic data such as roadkill risk, species occurrence, and landscape resistance into spatial planning (Machado-Aguilera et al. 2024 ). Areas with high concentrations of roadkills and those where vegetation cover aligns with known wildlife movement corridors, especially for felids, should not only be prioritized for habitat restoration but also for the explicit design and construction of wildlife crossings (Meza et al. 2019 ; Arango-Lozano and Patiño-Siro 2020 ; Pinto et al. 2020 ; Grilo et al. 2021 ; Meza 2023). These crossings must be adapted to a variety of terrestrial species and, ideally, should include guiding structures such as fencing that prevent animals from accessing the road and instead direct them safely toward the designated crossing points, while integrating forest cover and environmental enrichment into these structures can further enhance their effectiveness (Meza et al. 2019 ; Arango-Lozano and Patiño-Siro 2020 ). Implementing such crossings is not just beneficial, it is an urgent necessity for mitigating the negative ecological impacts in the region. Declarations Competing interest and funding . We declare no conflicts of interest related to the content of this article. No external funding was received for the development of this research. All work, including data collection (with the assistance of local community members), analysis, and writing, was conducted independently by the authors. We gratefully acknowledge the support of local people during the fieldwork phase Author Contribution All co-authors JAL and KAC conceived and designed the study, performed data analyses, and wrote the manuscript. Acknowledgement We extend our sincere thanks to Laura Siabatto and Andrés Link for granting permission to use their images of wild felids from the Middle Magdalena region. We are especially grateful to Michel Cabra Parra for his valuable support during the spatial analyses conducted in this study. We also deeply appreciate the unconditional support of the local communities and residents along the Ruta del Sol highway, who generously shared information about roadkill events and felid movements. We acknowledge the contributions of people from the corregimientos and rural centers of Puerto Araujo, Puerto Parra, Campo 23, La Fortuna, and La Lizama (Santander); Puerto Boyacá, El Trique, and Dos y Medio (Boyacá); Puerto Libre (Antioquia); and Puerto Salgar (Cundinamarca). Finally, we offer special recognition to the Ipakarai Biological Station, a vital source of information on jaguar presence in the region. Data Availability Two supplementary materials are added with this manuscript. The first one includes general roiadkill data of all animals along the route. The second one includes the general occurrences of both found death in the road and alive by camera traps and citizen science records as iNaturalist. In the other hand we have repository information including raster files as: 1. Landscape permeability and 2. Roadkill hotspots, are available in OSF: https://osf.io/94saf/ References Arango-Lozano J, Patiño-Siro D (2020) Does the geometrical design of roads influence wildlife roadkills? Evidence from a highway in central andes of Columbia. Eur J Ecol 6(1):58–70. https://doi.org/10.17161/eurojecol.v6i1.13688 Bennett VJ (2017) Effects of road density and pattern on the conservation of species and biodiversity. 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J Environ Manage 277:111412. https://doi.org/10.1016/j.jenvman.2020.111412 Medrano-Vizcaíno P, Grilo C, Silva Pinto FA, Carvalho WD, Melinski RD, Schultz ED, González‐Suárez M (2022) Roadkill patterns in Latin American birds and mammals. Glob Ecol Biogeogr 31(9):1756–1783. https://doi.org/10.1111/geb.13557 Mendes-Oliveira AC, Peres CA, Maués PCRDA et al (2017) Oil palm monoculture induces drastic erosion of an Amazonian forest mammal fauna. PLoS ONE 12(11):e0187650. https://doi.org/10.1371/journal.pone.0187650 Meza FL, Ramos E, Cardona D (2019) Spatio-temporal patterns of mammal road mortality in Middle Magdalena Valley, Colombia. Oecol Austral 23(3). https://doi.org/10.4257/oeco.2019.2303.15 Meza-Joya FL (2023) Road Permeability Index as a tool for mitigation planning of road impacts on wildlife in Colombia: a case study using mammals. 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Colombia Check List 15(3):387–404. https://doi.org/10.15560/15.3.387 Paviolo A, De Angelo C, Ferraz KM et al (2016) A biodiversity hotspot losing its top predator: The challenge of jaguar conservation in the Atlantic Forest of South America. Sci rep 6(1):37147. https://doi.org/10.1038/srep37147 Pinto FA, Clevenger AP, Grilo C (2020) Effects of roads on terrestrial vertebrate species in Latin America. Environ Impact Assess Rev 81:106337. https://doi.org/10.1016/j.eiar.2019.106337 Porter JH, Dueser RD, Moncrief ND (2015) Cost-distance analysis of mesopredators as a tool for avian habitat restoration on a naturally fragmented landscape. J Wildl Manag 79(2):220–234. https://doi.org/10.1002/jwmg.829 Quintana I, Cifuentes EF, Dunnink JA et al (2022) Severe conservation risks of roads on apex predators. Sci Rep 12(1):2902. https://doi.org/10.1038/s41598-022-05294-9 Quintero-Ángel A, Osorio-Dominguez D, Vargas-Salinas F, Saavedra-Rodríguez CA (2012) Roadkill rate of snakes in a disturbed landscape of Central Andes of Colombia. Herpetol Notes 5:99–105 R Core Team (2025) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/ Rabinowitz A, Zeller KA (2010) A range-wide model of landscape connectivity and conservation for the jaguar, Panthera onca . Biol Conserv 143(4):939–945. https://doi.org/10.1016/j.biocon.2010.01.002 Reid RS, Thornton PK, Kruska RL (2004) Loss and fragmentation of habitat for pastoral people and wildlife in East Africa: concepts and issues. Afr J Range Forage Sci 21(3):171–181. https://doi.org/10.2989/10220110409485849 Rendall AR, Webb V, Sutherland DR, White JG, Renwick L, Cooke R (2021) Where wildlife and traffic collide: Roadkill rates change through time in a wildlife-tourism hotspot. Glob Ecol Conserv 27:e01530. https://doi.org/10.1016/j.gecco.2021.e01530 Shepard DB, Kuhns AR, Dreslik MJ, Phillips CA (2008) Roads as barriers to animal movement in fragmented landscapes. Anim Conserv 11(4):288–296. https://doi.org/10.1111/j.1469-1795.2008.00183.x Wang Y, Smith JA, Wilmers CC (2017) Residential development alters behavior, movement, and energetics in an apex predator, the puma. PLoS ONE 12(10):e0184687. https://doi.org/10.1371/journal.pone.0184687 Wolf C, Ripple WJ (2016) Prey depletion as a threat to the world's large carnivores. R Soc Open Sci 3(8):160252. https://doi.org/10.1098/rsos.160252 Zanin M, Palomares F, Brito D (2015) What we (don't) know about the effects of habitat loss and fragmentation on felids. Oryx 49(1):96–106. https://doi.org/10.1017/S0030605313001609 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.docx SupplementaryMaterial2.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7198781","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493002225,"identity":"ec78b208-7c78-4f7e-b016-7e453944796c","order_by":0,"name":"Julián Arango-Lozano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYPACCwglYWADJBkbDxChRQJKFaSBtDSQoIXhw2EwhVcL/+zDzx5+qZCQk28/e/CBhcF5u7Xth4G21NhE4zT+XJq5scwZCWPGnrxkAwmD28nbziQCtRxLy23ApecMg5m0ZJtEYjNDjpkESIvZAaAWxobDOLXIn2H/BtbSxv8GpOVcstn5h/i1GJzhMZP8CNTSIwG25YCd2Q0Cthie4SmTZgD6RULijTHQL8kJZjeAtiTg8YvcGfZtkj8qbOTk+3MMH0v8sbM3O5/+8MGHGhvc3gcCZh4YAxg9iWCVCXiUgwDjDxjjAwODPQHFo2AUjIJRMAIBAMZOW3qtG4u6AAAAAElFTkSuQmCC","orcid":"","institution":"Universidad de Caldas","correspondingAuthor":true,"prefix":"","firstName":"Julián","middleName":"","lastName":"Arango-Lozano","suffix":""},{"id":493002226,"identity":"91ac0d5f-09ef-41f9-80a7-a14e15878eb4","order_by":1,"name":"Karime Angarita-Corzo","email":"","orcid":"","institution":"Universidad de Antioquia UdeA","correspondingAuthor":false,"prefix":"","firstName":"Karime","middleName":"","lastName":"Angarita-Corzo","suffix":""}],"badges":[],"createdAt":"2025-07-23 17:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7198781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7198781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88108289,"identity":"8642750b-2240-407f-b15e-e11d21c2852d","added_by":"auto","created_at":"2025-08-01 13:00:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10979102,"visible":true,"origin":"","legend":"\u003cp\u003eRecords of both alive by camera traps nearby (\u0026lt;100 m) and roadkilled felids in the ruta del sol highway. a \u003cem\u003ePanthera onca\u003c/em\u003e, b \u003cem\u003ePuma concolor\u003c/em\u003e, c-d \u003cem\u003eLeopardus pardalis\u003c/em\u003e, e \u003cem\u003eL. wiedii\u003c/em\u003e, f \u003cem\u003eH. yagouaroundi\u003c/em\u003e. Photographs A-D and F by Julián Arango-Lozano; Photograph E by Laura Siabatto and Andres Link.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/7565079d0fb2c610092b0948.png"},{"id":88109197,"identity":"8b0bd803-746c-4327-8c87-695d6f9d3a0b","added_by":"auto","created_at":"2025-08-01 13:08:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":782530,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area along the Ruta del Sol highway, spanning from Puerto Salgar (Cundinamarca) to Barrancabermeja (Santander) in the Middle Magdalena Valley. The background layer represents forest cover versus non-forest areas, illustrating the degree of landscape fragmentation. Colored dots indicate recorded occurrences of different felid species, while black arrows mark the locations where roadkill events involving felids were documented. The precise location of the roadkilled felids is shown in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/43ee7594eec87e5e58ef9d65.png"},{"id":88108286,"identity":"e8910936-3a61-4cfc-ba1e-a901125392f6","added_by":"auto","created_at":"2025-08-01 13:00:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4784790,"visible":true,"origin":"","legend":"\u003cp\u003eResult of the analysis of \u003cstrong\u003ea\u003c/strong\u003e. Roadkill hotspots (spatial aggregation of roadkills); \u003cstrong\u003eb \u003c/strong\u003eLandscape permeability for felid species.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/59b1744e798f9ee8edc7c59c.png"},{"id":88110178,"identity":"e9568ee1-7e47-47d4-b297-5e6f3153c047","added_by":"auto","created_at":"2025-08-01 13:16:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":705002,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted risk probability in response to \u003cstrong\u003ea\u003c/strong\u003e, landscape friction and \u003cstrong\u003eb\u003c/strong\u003e, distance to roadkill hotspots for five felid species in the Middle Magdalena valley.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/a5c0480a05c64b62d47ba43b.png"},{"id":93266676,"identity":"5d1a13e0-38ec-4e90-9f33-4a0aed1c3e44","added_by":"auto","created_at":"2025-10-10 20:18:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17234236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/36fd722a-85e9-42c8-a9da-9b417e935de3.pdf"},{"id":88108319,"identity":"2b60c4dc-b559-40da-8d9e-47a61929452f","added_by":"auto","created_at":"2025-08-01 13:00:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23024649,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/71aa896972be9114a23092ec.docx"},{"id":88108291,"identity":"a6d5d7bb-4eef-4166-a2ae-aec6d81ffc32","added_by":"auto","created_at":"2025-08-01 13:00:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4954794,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7198781/v1/c534a6af1b739f56186c78c1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crossing through danger: Spatial drivers of roadkill risk for felids in the fragmented Middle Magdalena valley","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRoad infrastructure is an important topic of human development but often comes at a significant ecological cost (Ben\u0026iacute;tez-L\u0026oacute;pez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bennett \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Roads, highways, fragment habitats, alter animal behavior, and create barriers which can lead to population isolation (Forman and Alexander \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Shepard et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Among other, wildlife roadkill is the most direct and visible impact to biodiversity (Coffin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rendall et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which has been documented as a leading cause of mortality for various species globally (Pinto et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Grilo et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the way the roads are designed, including their geometry and placement, can exacerbate this issue, making certain areas more hazardous for animals attempting to cross (de Freitas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Arango-Lozano and Pati\u0026ntilde;o-Siro \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In regions with high biodiversity, such as Colombia, the consequences of roadkill can be particularly severe, leading to the decline of already vulnerable species (Quintero-\u0026Aacute;ngel et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Medrano-Vizca\u0026iacute;no et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHabitat fragmentation, increased by infrastructure development, leads wildlife to move through increasingly restricted and degraded habitats (Reid et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Coffin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Di Giulio et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Important predators like Felids, which require large territories, are particularly susceptible to roads posed dangers when crossing fragmented landscapes (Crooks et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zanin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Blackburn et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fragmentation not only forces these animals closer to roadways but also increases their exposure to additional stressors such as noise, pollution, and human activity, which further elevate their risk of roadkill (Forman and Alexander \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ben\u0026iacute;tez-L\u0026oacute;pez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bennett \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Colombia, the Middle Magdalena Valley region is an important reservoir of tropical forest (Myers \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ovalle-Pacheco et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), home to several protected areas, including the Serran\u0026iacute;a de los Yarigu\u0026iacute;es, Reserva Forestal del Magdalena, Serran\u0026iacute;a de las Quinchas, and the Humedal San Silvestre (Donegan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ovalle-Pacheco et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gaona \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These areas are crucial for the conservation of a rich diversity of mammalian species, particularly felids like \u003cem\u003eLeopardus pardalis\u003c/em\u003e, \u003cem\u003eL. wiedii\u003c/em\u003e, \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e, \u003cem\u003ePuma concolor\u003c/em\u003e, and \u003cem\u003ePanthera onca\u003c/em\u003e, species with a crucial role in maintaining ecosystem functionality when controlling prey populations, which helps regulate the balance within trophic levels (Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;). However, the region is also a focal point for extensive industrial and infrastructural development, including projects by fossil energy, various solar photovoltaic parks, and ongoing highway construction (Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meza 2023). The constant flux of heavy trucks on major roads, such as the Ruta del Sol highway in national routes (RN) 4510, 4511, 4513, exacerbates the risk of roadkill for these wide-ranging species (Diaz-Pulido and Ben\u0026iacute;tez \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meza 2023). As consequences, different felids have been detected roadkilled in the ruta del sol highway (9 individuals of \u003cem\u003eL. pardalis\u003c/em\u003e, 4 of \u003cem\u003eH. yagouaroundi\u003c/em\u003e and 1 of \u003cem\u003eP. concolor;\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and others have been near detected to the road but not roadkilled (\u003cem\u003eL. wiedii\u003c/em\u003e, \u003cem\u003eP. onca\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFelids in the known fragmented region of the Magdalena Medio require larger conserved zones compared to other mammals to maintain their biological cycles (Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003ePanthera onca\u003c/em\u003e, has been recognized by international conservation efforts with the creation of corridors as part of broader conservation goals (Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to their essential role as keystone species, Felids conservation is not just about protecting individual species but about safeguarding the entire ecosystem they inhabit (Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meza 2023). Special attention must be given to these species, as their decline can lead to cascading effects that disrupt biodiversity and ecosystem processes (Blackburn et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe combination of habitat fragmentation and road infrastructure development in the biodiversity-rich Middle Magdalena region presents a significant threat to felid conservation (Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Blackburn et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e Meza 2023). Therefore, the objective of this research was to identify the most vulnerable areas for felids to be probable roadkilled within the fragmented landscape of green corridors in the Magdalena Medio region, focusing specifically on the Ruta del Sol highway. The study concentrated on the stretch between the intersections with Puerto Salgar (Cundinamarca) and Barrancabermeja (Santander) in Colombia's inter-Andean valleys, using records of both live sightings and roadkill incidents involving felid species.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStudy area\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe conducted the study in Colombia\u0026rsquo;s Middle Magdalena Valley region, focusing on areas along and surrounding national routes 4510, 4511, and 4513; collectively known as part of the Ruta del Sol (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), specifically between the municipalities of Puerto Salgar (Cundinamarca) and Barrancabermeja (Santander). The region consists of a mosaic of lowland alluvial floodplains and remnants of tropical moist forest (Myers \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gaona et al; 2025). The landscape is highly fragmented due to extensive cattle and agricultural activities such as oil palm plantations; furthermore, intense anthropogenic pressure from infrastructure and energy projects, including oil extraction, the development of solar photovoltaic parks, and the recent expansion of highways of which the Ruta del Sol is a central component (Meza 2023; Montes-Rojas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gaona et al; 2025). Climate with high annual rainfall concentrated in two main rainy seasons (typically between April - May and September\u0026ndash;November), and average temperatures ranging from 26\u0026deg;C to 30\u0026deg;C (Jaimes et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Montes-Rojas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gaona et al; 2025).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRoadkill hotspots\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFrom December 2022 to March 2025, we collected 396 roadkill events of vertebrates in the ruta del sol highway (Puerto Salgar \u0026ndash; Barrancabermeja extension). The records were collected with a periodic survey of 2 times per week one person in a car at 70 km/h searching for carcasses. However, given that the highway spans more than 250 km, we also incorporated data provided by residents who submitted photographs of roadkilled animals along with precise geographic coordinates. When possible, for verification purposes, we asked contributors to specify the exact kilometer marker point (PK) which also is indicated along the roadside every kilometer. We only included roadkill records based on our own direct observations and reports from residents that included clearly identifiable carcasses\u0026rsquo; photographs and locations.\u003c/p\u003e\u003cp\u003eWith the known roadkill data, we classified the records between taxonomic and date encounters. To identify roadkill hotspots, we applied the kernel density analysis using the online version of ArcGis (Esri, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Rather than relying on specialized software such as Siriema 2.0 (Coelho et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which often emphasizes detailed and site-specific modeling, we aimed to produce a broader, more generalized spatial output for the identification of areas with a higher concentration of roadkill events along the highway based on point density patterns; since Kernel density estimation (KDE) calculates the density of point features in a neighborhood around each output raster cell, creating a surface of intensity values (Silverman, 1986; Medinas et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo reflect a relative likelihood of roadkill occurrence across the study area, we rescaled the values of the resulted raster between 0 and 1, representing a continuous probability surface; it is from lower to higher probability zones. This generalization was intentional, as it enabled the integration of roadkill risk into wider geographic space.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLandscape permeability (friction)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo assess landscape permeability for felid species in the nearby region: \u0026ldquo;\u003cem\u003eLeopardus pardalis\u003c/em\u003e, \u003cem\u003eL. wiedii\u003c/em\u003e, \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e, \u003cem\u003ePuma concolor\u003c/em\u003e, and \u003cem\u003ePanthera onca\u003c/em\u003e\u0026rdquo;, there was conducted a friction analysis using ArcGIS Online (Esri, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), combining multiple spatial layers into a single raster surface quantifying the resistance to movement (Gietl et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Porter et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Each input layer represented a key ecological or anthropogenic factor influencing felid mobility: land cover types (Lc), proximity to core areas (Ca), distance between habitat fragments (Df), and hydrological features (Hr). Each category within these variables was assigned a resistance value ranging from 1 (low difficulty of movement) to 5 (high difficulty), based on the constructed agreement suitability criteria for felids in our study such as: habitat preference to Primary forests, low fragmentation tolerance, and high sensitivity to human disturbance, with a 40 km\u003csup\u003e2\u003c/sup\u003e minimum patch for inhabiting (Zanin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, dense riparian forests and gallery woodlands were assigned the lowest resistance (1), while urban areas and large-scale agricultural zones received the highest (5), see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These reclassified layers were then weighted and summed to produce a composite friction surface, where lower values represent areas of higher habitat suitability and connectivity, and higher values highlight regions of greater movement difficulty or fragmentation. The extension of the analysis occupies a 100 km buffer from the middle of the highway. All analyses were performed in R (R Core Team \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of spatial layers and associated friction values used in the friction -connectivity analysis. Each category within the layers was assigned a friction value from 1 (low resistance) to 5 (high resistance), representing its influence on the agreement felid species movement across the landscape.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eLand Cover Types - Lc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFriction Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreferred habitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGallery and/or riparian forest, Dense high floodplain forest, Palm groves\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemi-natural cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSandbanks, Tall secondary vegetation, Low secondary vegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately altered cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHerbaceous vegetation (various types), Oil palm plantations, Forest plantations, Rivers (50 m buffer), Swamp areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtificial water/pasture areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtificial water bodies, Aquaculture ponds, Oxidation lagoons, Clean pastures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighly transformed areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntensive agricultural areas, Roads, Urban and industrial areas, Degraded or burned zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCore Areas - Ca\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eFriction Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInside core areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003einside the core zones (habitat patches of \u0026gt;\u0026thinsp;40 km2 for our felids agreement)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutside core areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoutside the core zones (habitat patches of \u0026gt;\u0026thinsp;40 km2 for our felids agreement)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to Preferred Habitat Fragments - Df\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance Range\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eFriction Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;250 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e250 m \u0026ndash; \u0026lt; 500 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e500 m \u0026ndash; \u0026lt; 750 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e750 m \u0026ndash; \u0026lt; 1000 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1000 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWater Resource Type (hydric resource - Hr)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eFriction Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatural water bodies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNatural lagoons, lakes, and swamps, rivers (50 m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemi-natural wetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwampy areas, aquatic vegetation, natural sandy areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtificial water bodies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtificial water bodies, aquaculture ponds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltered water systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOxidation lagoons, canals, peat bogs, glacial and nival zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-related covers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoverages unrelated to the water resource\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: All layers in this analysis were downloaded from IDEAM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://experience.arcgis.com/experience/568ddab184334f6b81a04d2fe9aac262/page/Datos-Abiertos-Geogr%C3%A1ficos-/\u003c/span\u003e\u003cspan address=\"https://experience.arcgis.com/experience/568ddab184334f6b81a04d2fe9aac262/page/Datos-Abiertos-Geogr%C3%A1ficos-/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), at a resolution 1:100000 from the year 2022.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRoadkill and landscape permeability risk\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo detect whether felid species occurring near the \u003cem\u003eRuta del Sol\u003c/em\u003e highway are at greater risk of roadkill, we developed a risk model based on the spatial occurrence of these species in relation to key landscape variables. The methodological approach included the following steps:\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData collection of species occurrences\u003c/span\u003e. A comprehensive database was built using Presence records of felid species obtained from public platforms such as iNaturalist, covering a time frame from 2018 to 2025. Each record was reviewed to ensure spatial accuracy and appropriate use; and Roadkill records, collected through systematic field surveys conducted between 2022 and 2025 along different sections of the \u003cem\u003eRuta del Sol\u003c/em\u003e. In total we recorded 15 roadkill events as follows: 9 of \u003cem\u003eLeopardus pardalis\u003c/em\u003e, 5 of \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e and 1 of \u003cem\u003ePuma concolor\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) by surveying and 169 observation events from opportunistic encounters (camera trap) and iNaturalist records (mainly by camera traps) in all the 5 recognized felid species in the Middle Magdalena Valley (\u003cb\u003eSupplementary material 2\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/94saf/\u003c/span\u003e\u003cspan address=\"https://osf.io/94saf/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of the roadkilled felid species in the ruta del sol Highway. PK\u0026thinsp;=\u0026thinsp;Kilometer marker point, NR\u0026thinsp;=\u0026thinsp;National route.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"+\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLongitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19/06/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e79\u0026thinsp;+\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,488075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,260016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27/09/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e22\u0026thinsp;+\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,642408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5,482914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23/10/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e133\u0026thinsp;+\u0026thinsp;580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,658974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7,045297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24/12/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e23\u0026thinsp;+\u0026thinsp;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,871522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,743528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13/01/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e33\u0026thinsp;+\u0026thinsp;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,930012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,666792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14/01/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e91\u0026thinsp;+\u0026thinsp;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,930012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,666792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26/01/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e121\u0026thinsp;+\u0026thinsp;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,101242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,521126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1/03/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e134\u0026thinsp;+\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,256577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,478132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23/05/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e125\u0026thinsp;+\u0026thinsp;900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,561862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,184405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27/05/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e113\u0026thinsp;+\u0026thinsp;000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,553307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,096157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14/06/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e33\u0026thinsp;+\u0026thinsp;900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,551989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,072828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26/08/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePuma concolor\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e108\u0026thinsp;+\u0026thinsp;000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-74,622717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5,722991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9/11/2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e104\u0026thinsp;+\u0026thinsp;700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,82105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,778662\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1/03/2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e134\u0026thinsp;+\u0026thinsp;000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,71403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,938518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22/04/2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c3\"\u003e\u003cp\u003e140\u0026thinsp;+\u0026thinsp;000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-73,686071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,998461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eConstruction of a Spatial Occurrence Matrix\u003c/span\u003e. Using both presence and roadkill data, we created a spatial occurrence matrix that allowed us to identify areas where species are likely to occur. This matrix was georeferenced and overlaid with Distance to roadkill hotspots (areas of high roadkill density identified through kernel density analysis); and the landscape friction raster output, the continuous surface representing movement resistance across the landscape, where higher values indicate lower permeability for wildlife. All variables (distance to hotspot and landscape friction) were standardized (mean\u0026thinsp;=\u0026thinsp;0, standard deviation\u0026thinsp;=\u0026thinsp;1) to facilitate interpretation of model coefficients and avoid scaling issues.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Modeling of Risk\u003c/span\u003e. We used a beta regression model (via the betareg function in R, Zeileis et al. 2016) with a logit link, appropriate for modeling proportional response variables constrained between 0 and 1. The response variable was the adjusted risk probability of a species occurring in areas with both high roadkill potential and varying levels of landscape friction. The predictors included: distance to hotspot; normalized landscape friction, species identity as a categorical variable, with \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e as the reference category. With the logit transformation the model formula is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:logit\\left({y}_{i}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}\\cdot\\:{x}_{1i}+{\\beta\\:}_{2}\\cdot\\:{x}_{2i}+{\\sum\\:}_{j=1}^{k}{\\gamma\\:}_{j}\\cdot\\:Species{j}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the risk probability (adjusted 0\u0026ndash;1) for observation \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:logit\\left({y}_{i}\\right)=log\\left(\\frac{y1}{1-yi}\\right)\\)\u003c/span\u003e\u003c/span\u003e, is the logit transformation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{1i}\\)\u003c/span\u003e\u003c/span\u003e​ is the standardized distance to hotspot.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{2i}\\)\u003c/span\u003e\u003c/span\u003e is the standardized landscape friction.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Species}_{i}\\)\u003c/span\u003e\u003c/span\u003e are dummy variables for species identity (reference: \u003cem\u003eH. yagouaroundi\u003c/em\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e ​ = Intercept (risk for the reference species at average conditions),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ\u003csub\u003e1​\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Effect of distance to hotspot,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ\u003csub\u003e2\u003c/sub\u003e​ = Effect of landscape friction,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eγ\u003csub\u003ej\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Coefficients for each species \u003cem\u003ejjj\u003c/em\u003e, comparing them to the reference species.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThis expression captures how each predictor (distance, friction, and species) influences the log-odds of the species being at risk in the landscape. All analyses were performed in R (R Core Team \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eRoadkill hotspots\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOut of the kernel density analysis, it was found 4 mainly sections in the road with a greater spatial aggregation of roadkills located in the middle of the route near to the localities of Puerto Boyac\u0026aacute; (Boyac\u0026aacute;), Puerto Araujo (Antioquia), Cimitarra (Santander), and the most important near to Puerto Parra (Santander); the hotspots are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. The regions were involved with greater accumulation of the species: \u003cem\u003eTamandua Mexicana\u003c/em\u003e with 20% of the total roadkills (396), \u003cem\u003eProcyon cancrivorus\u003c/em\u003e and \u003cem\u003eCerdocyon thous\u003c/em\u003e each with 10% of the roadkills; \u003cem\u003eHydrochoerus isthmius\u003c/em\u003e with 6.3% of the roadkills, and \u003cem\u003eCaiman crocodilus\u003c/em\u003e with 5.7% of the roadkills; the full description of the roadkill information can be accessed in \u003cb\u003eSupplementary Material 1\u003c/b\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/94saf/\u003c/span\u003e\u003cspan address=\"https://osf.io/94saf/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eLandscape permeability (friction)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe friction results showed more than 65% of the geographical space evaluated as with high very high resistance (\u0026gt;\u0026thinsp;4.5) it is spaces with agricultural/ urban areas; less than 20% of the geographical space as with very low friction (\u0026lt;\u0026thinsp;1), it is gallery/riparian forests; and les than 15% of the total space was represented by low (1\u0026ndash;2), moderate (2\u0026ndash;3) and moderate to high (3-4.5). So, it is recognized that the landscape surrounding the road is predominantly composed of areas with very high movement resistance, indicating low permeability and significant fragmentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRoadkill and landscape permeability risk\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhen generated the model of mixing effects of landscape permeability\u0026thinsp;+\u0026thinsp;distance to hotspots (i.e., roadkill risk), we found a strong positive effect of distance to hotspot (β\u0026thinsp;=\u0026thinsp;1.304, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting the expected results that individuals located closer from hotspots experience a higher predicted risk. A significant negative effect of landscape friction (β = -0.777, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that areas with higher resistance to movement are associated with lower risk probabilities. Among the species-level predictors (with reference level: \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e), only \u003cem\u003ePuma concolor\u003c/em\u003e exhibited a significantly lower risk probability (β = -0.414, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). The remaining species (\u003cem\u003eLeopadus wiedii\u003c/em\u003e, \u003cem\u003eL. pardalis\u003c/em\u003e, \u003cem\u003ePanthera onca\u003c/em\u003e) did not differ significantly from the reference species in terms of predicted risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the precision parameter (phi) was high and significant (φ\u0026thinsp;=\u0026thinsp;23.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating limited dispersion in the risk predictions and increased confidence in the estimates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of fixed effects from the beta regression model predicting felid roadkill risk based on landscape friction, distance to hotspots, and species identity. Reference species for analysis\u0026thinsp;=\u0026thinsp;\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-10.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to hotspot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLandscape friction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-18.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeopardus wiedii\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeopardus pardalis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanthera onca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePuma concolor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: Intercept\u0026thinsp;=\u0026thinsp;Distance to hotspot\u0026thinsp;+\u0026thinsp;Landscape friction; Significance codes: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe fragmentation processes in the Middle Magdalena Valley have long been recognized as a major driver of disrupted ecological connectivity among various animal species (Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Montes-Rojas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Where large felids such as the jaguar (\u003cem\u003ePanthera onca\u003c/em\u003e) are of particular concern due to their critical ecological roles (Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite significant international conservation efforts (Rabinowitz and Zeller \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), multiple studies have emphasized the ongoing vulnerability not only for jaguars but the four other felid species inhabiting the region, all of which require vast, continuous territories to fulfill their functional roles within ecosystems (Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meza et al. 2023). In highly fragmented landscapes, these felids may travel even greater distances to find essential resources, thereby increasing their exposure to anthropogenic threats such as the shown in this research, the roadkill and human-wildlife conflict (Zanin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEven with fragmented habitats, the Middle Magdalena may not sustain felids without their critical resources, among others, prey species (Zanin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wolf and Ripple \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although identified through roadkill records, species such as \u003cem\u003eTamandua mexicana\u003c/em\u003e, \u003cem\u003eHydrochoerus isthmius\u003c/em\u003e, \u003cem\u003eProcyon cancrivorus\u003c/em\u003e, \u003cem\u003eCerdocyon thous\u003c/em\u003e, and \u003cem\u003eCaiman crocodilus\u003c/em\u003e, which together account for more than 50% of all recorded roadkill events (see \u003cb\u003eSupplementary Material 1\u003c/b\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/94saf/\u003c/span\u003e\u003cspan address=\"https://osf.io/94saf/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), highlighting the ecological significance of the region, which serves as a key refuge for maintaining overall biodiversity and supporting the functioning of surrounding ecosystems, particularly for top predators as felids (Myers \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Montes-Rojas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur spatial analyses revealed that risk for felids increases near identified roadkill hotspots, and decreases in areas characterized by higher landscape friction, interpreted here as more fragmented or less permeable landscapes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This pattern suggests that felids preferentially move through more permeable and better-connected habitats, which unfortunately tend to coincide with areas of higher roadkill probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e); this is paradox but a recognized pattern in road ecology, where animals are more likely to cross the roads in areas that still provide ecological functionality (Coffin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Crooks et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bennet 2017; Blackburn et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meza et al. 2023).​\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhile in general the tendencies to risk are similar in all felid species, Our beta regression model indicates that \u003cem\u003ePuma concolor\u003c/em\u003e exhibits a significantly lower predicted risk of roadkill compared to \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e (reference species for the analysis), which may reflect behavioral adaptations such as greater avoidance of roads or preference for larger forested areas (Dickson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; lewis et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Carvalho et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, species like \u003cem\u003eLeopardus pardalis\u003c/em\u003e and \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e with expected avoiding modified habitats (Fischer et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bolze et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have been detected occupying disturbed environments (Dotta and Verdade \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Giordano \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Coronado-Quibrera et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fornitano et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); and in our records, the felids with more roadkill occurrences. In the other hand, although \u003cem\u003eLeopardus wiedii\u003c/em\u003e and \u003cem\u003ePanthera onca\u003c/em\u003e were not detected as roadkill victims, their presence near roads suggests potential risk, possibly mitigated by their elusive nature and preference for well-preserved habitats (Hodge \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, our observations to these last species recognize them in modified lands as oil palm crops, just as other studies have shown (Boron et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mendes-Oliveira et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe conservation of apex predators such as \u003cem\u003ePuma concolor\u003c/em\u003e and \u003cem\u003ePanthera onca\u003c/em\u003e is particularly crucial, their presence maintains ecosystem balance through regulation of prey populations and competitive interactions (Wang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The loss or decline of these species could trigger trophic cascades, leading to overpopulation of certain herbivores or mesopredators and further degradation of already fragile habitats (Paviolo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wolf and Ripple \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ordiz et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the Middle Magdalena Valley, biodiversity remains high but increasingly vulnerable, where the persistence of large felids contributes to maintaining the ecological processes and biodiversity functionality (Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is important to highlight that convergence of five felid species within the same fragmented landscape raises important ecological questions regarding resource competition and habitat carrying capacity (Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While current prey availability, as evidenced by roadkill records, seem matching for multiple of our felids, the long-term sustainability of these communities remains uncertain under ongoing habitat loss and fragmentation (Zanin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wolf and Ripple \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The high number of roadkills observed for species like \u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e (5 individuals) and \u003cem\u003eLeopardus pardalis\u003c/em\u003e (9 individuals) is alarming given the typically low population densities of felids and their slow reproductive rates (Wang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although these numbers may seem modest, even a few deaths can have significant demographic impacts on local populations, particularly for these wide-ranging carnivores (Paviolo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ordiz et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quintana et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe potential future detection of roadkilled individuals of larger and more endangered species, such as the jaguar, would signal an even greater conservation crisis, highlighting the urgency for immediate and effective action (Rabinowitz and Zeller 2022; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While conservation strategies are already being implemented across the Middle Magdalena region, including habitat restoration projects, the creation of private reserves, and the promotion of wildlife-friendly practices in agricultural landscapes (Rabinowitz and Zeller \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Boron et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Figel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Montes-Rojas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); their success will depend on their ability to incorporate dynamic data such as roadkill risk, species occurrence, and landscape resistance into spatial planning (Machado-Aguilera et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAreas with high concentrations of roadkills and those where vegetation cover aligns with known wildlife movement corridors, especially for felids, should not only be prioritized for habitat restoration but also for the explicit design and construction of wildlife crossings (Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arango-Lozano and Pati\u0026ntilde;o-Siro \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pinto et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Grilo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meza 2023). These crossings must be adapted to a variety of terrestrial species and, ideally, should include guiding structures such as fencing that prevent animals from accessing the road and instead direct them safely toward the designated crossing points, while integrating forest cover and environmental enrichment into these structures can further enhance their effectiveness (Meza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arango-Lozano and Pati\u0026ntilde;o-Siro \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Implementing such crossings is not just beneficial, it is an urgent necessity for mitigating the negative ecological impacts in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interest \u003cstrong\u003eand funding\u003c/strong\u003e.\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe declare no conflicts of interest related to the content of this article. No external funding was received for the development of this research. All work, including data collection (with the assistance of local community members), analysis, and writing, was conducted independently by the authors. We gratefully acknowledge the support of local people during the fieldwork phase\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll co-authors JAL and KAC conceived and designed the study, performed data analyses, and wrote the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe extend our sincere thanks to Laura Siabatto and Andr\u0026eacute;s Link for granting permission to use their images of wild felids from the Middle Magdalena region. We are especially grateful to Michel Cabra Parra for his valuable support during the spatial analyses conducted in this study. We also deeply appreciate the unconditional support of the local communities and residents along the Ruta del Sol highway, who generously shared information about roadkill events and felid movements. We acknowledge the contributions of people from the corregimientos and rural centers of Puerto Araujo, Puerto Parra, Campo 23, La Fortuna, and La Lizama (Santander); Puerto Boyac\u0026aacute;, El Trique, and Dos y Medio (Boyac\u0026aacute;); Puerto Libre (Antioquia); and Puerto Salgar (Cundinamarca). Finally, we offer special recognition to the Ipakarai Biological Station, a vital source of information on jaguar presence in the region.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eTwo supplementary materials are added with this manuscript. The first one includes general roiadkill data of all animals along the route. The second one includes the general occurrences of both found death in the road and alive by camera traps and citizen science records as iNaturalist. In the other hand we have repository information including raster files as: 1. Landscape permeability and 2. Roadkill hotspots, are available in OSF: https://osf.io/94saf/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArango-Lozano J, Pati\u0026ntilde;o-Siro D (2020) Does the geometrical design of roads influence wildlife roadkills? Evidence from a highway in central andes of Columbia. 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Oryx 49(1):96\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0030605313001609\u003c/span\u003e\u003cspan address=\"10.1017/S0030605313001609\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Conservation planning, Cougar, Habitat permeability, Jaguar, Landscape fragmentation, Ocelot, Spatial modeling, Road ecology","lastPublishedDoi":"10.21203/rs.3.rs-7198781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7198781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Middle Magdalena valley in Colombia shows great fragmentation processes, which threatens conservation values and may particularly impact key ecological species such as wide-ranging felids. This study evaluates the vulnerability of the five native felid species (\u003cem\u003eHerpailurus yagouaroundi, Leopardus pardalis\u003c/em\u003e, \u003cem\u003eL. wiedii, Panthera onca\u003c/em\u003e and \u003cem\u003ePuma concolor\u003c/em\u003e) to roadkill along a 250 km stretch of the Ruta del Sol highway. We combined roadkill data with presence records from field surveys, camera traps and citizen science platforms between 2018 and 2025. Spatial analyses included kernel density estimation of roadkill hotspots and a friction surface model to quantify landscape permeability. A beta regression model assessed the relationship between roadkill risk, proximity to hotspots, landscape resistance, and species identity. We detected four roadkill hotspots associated with a lower landscape resistance, particularly around Puerto Parra and Puerto Boyac\u0026aacute;. \u003cem\u003eP. concolor\u003c/em\u003e showed a significantly lower risk of roadkill compared to \u003cem\u003eH. yagouaroundi\u003c/em\u003e, while other species showed similar risk levels. More than 65% of the landscape was classified as highly resistant to movement, indicating substantial fragmentation. Notably, roadkill hotspots overlapped with permeable habitat corridors, creating a conservation paradox where areas suitable for wildlife movement are also the most dangerous. We highlight the urgent need for spatially explicit mitigation measures, including wildlife crossings with guiding fences and habitat restoration in key corridors. As the convergence of five felid species suggests potential for interspecific competition and cumulative impacts, targeted conservation efforts are essential to maintain ecosystem integrity.\u003c/p\u003e","manuscriptTitle":"Crossing through danger: Spatial drivers of roadkill risk for felids in the fragmented Middle Magdalena valley","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 13:00:35","doi":"10.21203/rs.3.rs-7198781/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb9659d7-109a-428e-9bcf-e7116a5b8a7b","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-10T19:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-01 13:00:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7198781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7198781","identity":"rs-7198781","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}