Population density of White-tailed deer

in a protected area of the Ecuadorian Andes

Michelle Alexandra Rodríguez-Noroña1,2,3,4* , David Andrés Herrera-Orellana5 , Paúl Monar-Barragán2 ,
Juan Sebastián Restrepo-Cardona2,6 , Evelyn Edith Araujo2 , and Iván Vinicio Jácome-Negrete1 .

1Universidad Central del Ecuador. Facultad de Ciencias Biológicas. Numa Pompilio Llona y Yaguachi.170303, Quito-Ecuador. E-mail: ivjacome@uce.edu.ec (IVJ-N); marodriguezn04@gmail.com (MAR-N).

2Fundación Cóndor Andino Ecuador. José Tamayo y Lizardo García. 170523, Quito-Ecuador. E-mail: hpmonarb@gmail.com (PM-B); eearaujoll@gmail.com (EEA); jsrestrepoc@gmail.com (JSR-C); marodriguezn04@gmail.com (MAR-N).

3Instituto Nacional de Biodiversidad (INABIO). Calle Rumipamba 341 y Av. de los Shyris, PB 17-07-8976. Quito. Ecuador. E-mail: marodriguezn04@gmail.com (MAR-N).

4Fundación de Conservación Jocotoco. Av. Amazonas N40-80 y UNP, Piso 7. E-mail: marodriguezn04@gmail.com (MAR-N).

5Universidad Técnica Particular de Loja. San Cayetano Alto, C. París, Loja-Ecuador. E-mail: daho698@gmail.com (DAH-O).

6Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, USA. E-mail: jsrestrepoc@gmail.com (JSR-C).

*Corresponding author: marodriguezn04@gmail.com

The white-tailed deer (Odocoileus virginianus ustus) is an ecologically important species as it is part of the diet of carnivores and scavengers. However, there is little research on its population density in the Ecuadorian Andes. We evaluated white-tailed deer population density during two consecutive years (i.e., 2023 and 2024) in three different cover types (i.e., shrubland, grassland, and sandy areas) in the Área de Conservación Hídrica (ACH) Antisana. Data were collected by direct observational counts of individuals in line transects of at least ١ km, between ٨:٠٠ and ١٧:٠٠ h. The average population density was ١٢.٦١ ind km-2 in 2023 and 14.25 ind km-2 in 2024, although these differences were not significant. We also found no differences in deer densities across cover types, although this does not imply a homogeneous distribution of the species in the protected area. There is a clear need for long-term monitoring programs to understand the population dynamics of white-tailed deer and establish management strategies tailored to the unique conditions of each territory. We recommend implementing systematic protocols for estimating white-tailed deer population densities based on direct observation methods, as these provide reliable information on the number of individuals.

Keywords: Antisana, Cervidae, Paramo, Line transects, Odocoileus virginianus ustus, Population

El venado cola blanca (Odocoileus virginianus ustus) es una especie importante en los ecosistemas naturales, la cual hace parte de la dieta de carnívoros y carroñeros. Sin embargo, existen pocos estudios sobre su densidad poblacional en los Andes. Evaluamos la densidad poblacional del venado de cola blanca durante dos años consecutivos (i.e., 2023 y 2024) en 3 tipos de cobertura distintos (i.e., arbustal, herbazal y arenal) en el Área de Conservación Hídrica (ACH) Antisana. Los datos fueron obtenidos mediante conteo directo por observación de individuos en transectos lineales de al menos 1 km, entre las 08:00 y 17:00 h. La densidad poblacional promedio de venados fue 12.61 ind km-2 en 2023 y 14.25 ind km-2 en 2024, aunque dichas diferencias no fueron significativas. Tampoco encontramos diferencias en las densidades de venados en los distintos tipos de cobertura, aunque esto no implica una distribución homogénea de la especie en el área protegida. Es necesario realizar programas de seguimiento a largo plazo para conocer la dinámica poblacional del venado de cola blanca y establecer estrategias de manejo que se ajusten a las condiciones propias de cada territorio. Recomendamos implementar protocolos sistemáticos para la estimación de las densidades poblacionales del venado de cola blanca basados en métodos de observación directa, dado que estos proporcionan información confiable sobre el número de individuos.

Palabras clave: Antisana, Cervidae, Odocoileus virginianus ustus, Páramo, Población, Transectos lineales

The tropical Andes are a global biodiversity hotspot due to their high levels of endemism and ongoing habitat loss (Myers et al. 2000; Báez et al. 2016). High Andean ecosystems, particularly páramos, harbor remarkable plant diversity and play a key role in species evolution due to their unique biogeographical conditions (Arroyo and Cavieres 2013). In addition, these ecosystems provide essential services, including carbon storage, water regulation, and climate refugia for vulnerable species (Cincotta et al. 2000; Arroyo and Cavieres 2013). In Ecuador, páramo ecosystems are also fundamental for water supply, agriculture, and hydroelectric power generation, despite covering a small proportion of the national territory. However, they are increasingly threatened by anthropogenic pressures such as agricultural expansion, overexploitation, and population growth (Crespo et al. 2010; Etter et al. 2017; Sierra et al. 2021).

The ecological importance of white-tailed deer (Odocoileus virginianus ustus) is due at least in part to their role as a food source for other wild animals. Changes in deer abundance can cause variation in the diet of the cougar (Puma concolor), which can trigger predation of domestic animals, thus causing human-wildlife conflicts (Fulbright and Ortega 2007; Piña and Trejo 2014; Tellkamp et al. 2019). Likewise, artiodactyls are an important food source for obligate and facultative scavengers (Lambertucci et al. 2009; Perrig et al. 2017). However, its population status remains unknown, limiting the understanding of its potential effects on threatened species such as the condor, whose populations are declining due to human pressures (Restrepo-Cardona et al. 2022).

The methods used to estimate white-tailed deer population density include aerial surveys, mark-recapture techniques, the use of camera traps and remote thermal imaging, and fecal counts. Remote surveys are notable for their cost-effectiveness and efficiency (LaRue et al. 2007; Urbanek and Nielsen 2012). Counting individuals through direct observation is especially useful in areas with relatively high abundances and flat, open terrain that offers good visibility (Beltrán and Díaz 2010; Mandujano 2014). However, estimates of the species’ abundance and population size are often made using indirect methods such as counting feces in fixed transects (Ortiz-Martinez et al. 2005; Piña and Trejo 2014; Mandujano 2016). Nevertheless, this method provides a relative density index due to variation in defecation and feces decomposition rates caused by intrinsic and extrinsic factors (Galindo-Leal 1992; Pérez-Mejía et al. 2004; Navarro 2005; Mandujano 2014). Lower defecation rates can cause individuals to be overestimated (Mandujano 2014). In addition, if the substrate and environmental conditions are not suitable, feces may not be preserved long enough to be recorded (Navarro 2005). Thus, estimates of deer density may vary depending on the sampling methods used (Hewitt 2011). Nevertheless, there is little knowledge about the ecology and threats facing the white-tailed deer in the Ecuadorian páramos. Domestic dogs represent a significant threat to the species (Zapata-Ríos and Branch 2018; Restrepo-Cardona et al. 2025). Similarly, hunting and wildlife trafficking are latent threats to the species (Sánchez 2009). Although the population density of a species is crucial for determining its conservation status and establishing effective management and conservation strategies (Mandujano et al. 2010), the information available on the population status of the white-tailed deer is scarce in Ecuador. Albuja (2007) studied the population density of this species in the Oyacachi-Papallacta and Antisana páramos, reporting a density of 1.6 ind km-2. On the other hand, Tellkamp et al. (2019) evaluated the population density in the Área de Conservación Hídrica (ACH) Antisana and reported population densities of deer that ranged between ٧.٩١ ind km-٢ and ١١.٣١ ind km-٢ (Tellkamp et al. ٢٠١٩).

Therefore, it is crucial to determine the population status of white-tailed deer in the Ecuadorian Andean páramos, given that changes in population density can affect the populations of native species that feed on these animals (e.g., puma, Andean condor) and thus also the ecosystem services that this biodiversity provides to people (Rojas ٢٠١٠; Arenas ٢٠١١; Hansen et al. ٢٠١٧; Chávez et al. ٢٠٢٢). Hence, the objective of this study was to evaluate the population density of white-tailed deer for two consecutive years (i.e., ٢٠٢٣ and ٢٠٢٤) and in three different cover types (i.e., shrubland, grassland, and sandy area) in the ACH Antisana.

Materials and methods

Study area. The study was conducted in the ACH Antisana located in the provinces of Napo and Pichincha (Figure 1), which covers an area of 8487 ha. The ACH Antisana is bordered to the north by the Chakana Reserve, to the west by private land, and to the south and east by Antisana National Park (FONAG 2019). Its altitude ranges from 3720 to 4640 meters above sea level, and it has an average annual rainfall of 1098 mm (Sklenář et al. 2015; Bécquer 2022). In the páramos of Ecuador, annual precipitation has a bimodal pattern with wet seasons from February to May and from October to December (Hofstede and Mujica 2002). In the study area, rainfall is concentrated between December and June, with a drier period from July to November (Pérez and Zapata 2025). The area provides water for consumption to 2,6 million inhabitants of the Metropolitan District of Quito (Crespo et al. 2010). Historically, land use in the site was dedicated to intensive livestock farming, with cattle, sheep, and horses. By 2012, there were an estimated 1500 sheep and 6000 cattle head in the area (FONAG 2024). However, in an effort to conserve its water sources and biodiversity, the area was declared an ACH in 2012, and management actions have subsequently focused on reducing livestock impacts and promoting ecological restoration (Crespo et al. 2010; Grubb et al. 2020).

The most representative ecosystems of the ACH Antisana are (1) the paramo grassland, characterized by its herbaceous appearance and evergreen phenology, and dominated by Agrostis sp., Calamagrostis sp., and Cortaderia sp. (MAE 2013; Bécquer 2022). And (2) the floodable paramo grassland is distinguished by its flooding regime, with the presence of Lophozia laxifolia and Cortaderia sericantha plants that form cushions and floating vegetation on compact, thick soil that retains moisture and produces conditions of impermeability and inefficient drainage, which favors the formation of peat bogs and swamps (MAE 2013).

Field data collection. To estimate the population density of white-tailed deer, the direct counting method was used by observing individuals in linear transects. Sampling was carried out between October and November 2023 and between October and December 2024, in accordance with the schedule and logistical framework of the monitoring project under which this study was conducted. The sampling was carried out in three types of cover types: grassland, shrubland, and sandy areas, which cover an area within the ACH Antisana of 72.92 km², 8.89 km², and ٢.٤٣ km², respectively (Figure ٢). The three cover types are spatially distinguishable within the study area but are not completely isolated, allowing movement of white-tailed deer among them. Therefore, cover types were considered as sampling categories to evaluate relative differences in density rather than independent population units. A total of ٢٧ linear transects were established based on spatial coverage, representation of the different cover types and field accessibility within the study area, ensuring an adequate sampling effort while maintaining logistical feasibility and consistency between surveys. The transects had variable widths and lengths of ٢ km for grassland cover types and ١ km for shrubland and sandy area. To avoid sampling bias, a stratified random sampling design was used, where transects were randomly located within each cover type using previously defined polygons (Terán-Valdez et al. ٢٠١٨). Random points were generated in QGIS and used as starting locations, with the number of transects proportional to cover type area. Transect orientation was randomly assigned among cardinal directions (Mandujano ٢٠١٤). A minimum distance of ١ km between transects was maintained to avoid overlap, and any transect not meeting spatial criteria was re-randomized. The width of the transects was determined by the position of the deer observed. In addition, to reduce possible overlaps in observations, a minimum distance of ١ km between transects in the same cover type was considered to reduce the likelihood of repeated detections. While specific home range estimates for this species in páramo ecosystems are unavailable, studies in tropical environments have reported home ranges ranging from ٠.١٢ to ٠.٩ km² (Contreras-Moreno et al. ٢٠٢١).

Each transect was surveyed once a year, between ٨:٠٠ a.m. and ٥:٠٠ p.m., as outside this time frame deer tend to gather to feed and rest. Each transect was surveyed on foot by two researchers, ensuring that a set direction was followed at a constant speed of ٢ km/h and without backtracking (Figure ٣). All deer on both sides of the transect were recorded (Mandujano ٢٠١٤; Narváez and Zapata-Ríos ٢٠٢٠). When an individual was detected, the perpendicular distance of the animal from the transect was recorded using a LEUPOLD-RX-١٠٠٠i/TBR rangefinder, and in the case of grouped deer, the perpendicular distance from the transect to the center of the group was measured (Gallina et al. ٢٠١٤; Adams et al. ٢٠٢٠). No sampling was carried out on days with high rainfall because rain reduces the ability to detect animals, while on days with light or passing rain, the surveys were carried out as normal (Adams et al. ٢٠٢٠; Narváez and Zapata-Ríos ٢٠٢٠).

Statistical analysis. Deer density was estimated using alternative models, which were chosen based on a detection function adjusted to the data from perpendicular distance measurements to individuals or groups of individuals in each transect (Thomas et al. ٢٠١٠). The R package “Distance” (Buckland et al. ٢٠١٥) was used, which provides four models [unif (unif), half-normal (hn), hazard-rate (hr), and negative exponential] for the detection function, and also allows the addition of series adjustment terms [(cosine (cos), hermite polynomial (herm), simple polynomial (poly), and null adjustments] to the model when the simple model (“key function”) alone does not provide an adequate fit. The key function ‘unif’ was used. It can be used to perform strip transect analysis, but it is also useful for analyzing linear transect data if series adjustment terms are added (Buckland et al. ٢٠١٥). Likewise, the ‹hazard rate› function was used because it has a ‘shoulder’ (Buckland et al. ٢٠١٥). However, the key function ‘negative exponential’ was not used because it does not have a ‘shoulder’ (Buckland et al. ٢٠١٥). Next, a comparative analysis was performed between different selected models with their respective series adjustments: unif (cos, poly), half-normal (cos, herm, poly), and hazard-rate (cos, herm, poly), to find the appropriate detection function.

A plausible and parsimonious model was selected based on the Akaike›s information criterion (AIC), goodness of fit (Cramér-von Mises test and the Quantile-Quantile plot) and the shape criterion of the detection function (presents a ‘shoulder’). The model was chosen according to the criterion of parsimony, i.e., the one with the lowest AIC (Martínez et al. ٢٠٠٩). The Cramér-von Mises test was used to evaluate the difference between the empirical and theoretical distributions, rejecting the null hypothesis ( < ٠.٠٥) (Buckland et al. ٢٠١٥). The Q-Q plot compared the empirical distribution function (EDF) with the theoretical cumulative distribution function (CDF), considering the model to be inadequate if the points deviated from the reference line (Miller et al. ٢٠١٩). Finally, the density per m² was calculated and transformed to ind km-2 for the three cover types in 2023 and 2024. 

Prior to comparing the annual population density and density per cover type, the Shapiro-Wilk normality test was applied, which indicated that the data did not follow a normal distribution. The Wilcoxon test was used for both cases. To compare the population density between cover types in 2023 and 2024, the Kruskal-Wallis test was used separately for each year. All analyses were performed using statistical software R version 4.3.0 (R CoreTeam 2021).

Additionally, a heat map was generated in QGIS 3.10 (QGIS Development Team 2019). For this, the vector layers of transects by cover type were processed, incorporating density data and transect codes. These layers were then combined into a single file (.shp) and midpoints were generated along each transect. Next, the kernel density estimation algorithm was applied, using the midpoint layer as input, a kernel bandwidth of 2.4 km, and a pixel size of 30 m, and the entities were weighted according to density. Finally, a grayscale raster was obtained, the map was cropped to the study area, and the thermal intensity values were reclassified (QGIS Project 2020).

Results

A total of 45 km was covered in 27 transects in 2023 and 43 km in 26 transects in 2024. One transect could not be covered in 2024 due to logistical difficulties. During the surveys, 617 deer were recorded in 2023 and 470 deer in 2024. Group density decreased between 2023 and 2024, while individual density and expected group size increased. Similarly, estimated abundance was higher in 2024. Overall, the coefficients of variation were not high in both years, although slightly higher in 2024 (see Table 1). Model 4 (Hazard-rate + cos, see Supplementary 2) was selected as the most robust for both years, according to the AIC, Goodness of Fit, and Q-Q plot (Figure 4). Based on this model, average annual population densities were estimated, yielding values of 12.61 ind km-2 for 2023 and 14.25 ind km-2 for 2024, representing a population increase of 13.01% between the two years.

Population densities were calculated by cover type for each year (see Table 2). In both years, grassland concentrated the highest abundance and density of individuals, whereas shrubland and sandy areasshowed lower values. From 2023 to 2024, density increased in grassland and shrubland but decreased in sandy area. Coefficients of variation were higher in shrubland and sandy area, reflecting greater variability in these environments. In 2023, the shrubland recorded the highest density of deer (14.07 ind km-2), while the sandy areas had the lowest density. In 2024, the highest density was recorded in grassland cover type and the lowest density in sandy areas. At the transect level, in 2023, transect TB1-23 had the highest density with 49.15 ind km-2, whereas no individuals were recorded in transects TB5-23 and TE3-23. In 2024, transect TH14-24 showed the highest density with 89.05 ind km-2, and no deer were recorded in transects TB3-24, TH1-24, and TH9-24. 

The deer population density data for 2023 and 2024 did not follow a normal distribution (p < 0.05). The results of the Wilcoxon test (V = 170, p = 0.901) indicated that there were no significant differences between the deer densities recorded in 2023 and 2024 (Figure 5). No significant differences were detected in the densities recorded in both years in grassland (V = 76, p = 1), sandy (V = 4, p = 0.75), and shrubland (V = 6, p = 0.438) cover types (Figure 6). Kruskal Wallis tests (χ² = ٠.٦٨٤, d.f. = ٢, p = ٠.٧١٠) indicated that there were no significant differences between the densities recorded in the three cover types in ٢٠٢٣ (Figure ٧) or in ٢٠٢٤ (χ² = ٠.١٦٦, d.f. = ٢, p = ٠.٩٢٠) (Figure ٨).

The shrubland had deer densities ranging from ٠ to ٤٩ ind km-2 in 2023 and from 0 to 50 ind km-2 in 2024. The sandy areas recorded values between 0 and 14 ind km-2 in 2023 and between 2 and 8 ind km-2 in 2024. The grassland recorded values between 0 and 45 ind km-2 in 2023 and between 0 and 89 ind km-2 in 2024. In 2023, the highest densities (up to 49 ind km-2) were concentrated in the central region of the study area, decreasing progressively towards the west and north, where no individuals were recorded (Figure 9). The southwest zone had areas with densities of up to 45 ind km-2, while in the southeast, a small area with densities of up to 24 ind km-2 was identified. In 2024, the general pattern remained the same, with the highest densities (up to 89 ind km-2) located in the southwest region of the study area, followed by the central region. In the southeast, an area with densities of up to 52 ind km-2 was recorded, while the north and northwest continued to show low or zero densities.

Discussion

Our findings differ slightly from those reported for 2018 and 2019 in the ACH Antisana, where using direct methodologies (counts at observation points) and indirect methodologies (fecal accumulation rate, standing fecal culture, and remote fecal sampling), deer population densities ranging from 7.91 ind km-2 to 11.31 ind km-2 were reported (Tellkamp et al. 2019). The differences in results could be due to the sampling times used. White-tailed deer are considered a facultatively or semi-gregarious species, forming small and variable groups depending on season, sex, and behavioral context (Saalfeld et al. 2008). Because variation in group size can influence the precision of density estimates obtained through distance sampling, surveys conducted when deer are less aggregated may facilitate the detection of a greater number of groups and reduce variability in group size (Galindo-Leal and Weber 1998; Thomas et al. 2010; Buckland et al. 2015). The sampling carried out by Tellkamp et al. (2019) took place between 06:00 and 09:00 hrs and between 15:00 and 18:00 hrs. In contrast, our observations were made between 08:00 and 17:00 hrs, thus avoiding the species’ peak grouping times, as population density estimates can be affected by variability in the size of the groups observed. This strategy may have facilitated the detection of more groups of smaller sizes, potentially improving to more accurate estimates.

Nevertheless, the density values obtained differ significantly from the findings of Albuja (2007) between 1996 and 1997, who, through counts of individuals in transects and observation points, reported a population density of 1.6 ind km-2 in the Oyacachi-Papallacta and Antisana moorlands, a figure well below that reported in this study. These differences in deer density after almost two decades could be attributed to various factors. During the 1990s, deer poaching was a relatively common practice in Antisana National Park and its buffer zone, as was intensive livestock management (Albuja 2007). However, control and patrolling by park rangers have been progressively strengthened over the years (Terán-Valdez et al. 2018; Grubb et al. 2020).

The ACH Antisana has shown a favorable recovery process, which would explain the high number of deer in the area. During the last decade, the reduction of grazing pressure through livestock removal (Crespo et al. 2010; Grubb et al. 2020) and, in 2017, ecological restoration of sandy areas in this protected area began with the planting of native plants, including the species chocho silvestre (Lupinus pubescens), chilca (Baccharis caespitosa), achupalla (Puya clava-herculis), and chuquiragua (Chuquiraga jussieui) which are part of the deer’s diet (FONAG 2017). Also, since 2019, hunting control and the removal of introduced species, particularly livestock, camelids and horses have led to an increase in white-tailed deer populations (Crespo et al. 2010; Grubb et al. 2020). Also, the hunting control and the removal of introduced species, particularly livestock, camelids and horses have led to an increase in white-tailed deer populations (Grubb et al. 2020; Crespo et al. 2010). Additionally, 2021 human access to the protected area was restricted, which favored the recovery of vegetation cover (FONAG 2021). This entire process could explain the increase in the deer population and the ability of a recovered ecosystem to sustain these populations. This suggests that the recovery of ecosystem structure and function likely plays a key role in maintaining current deer population levels.

Our density findings are consistent with those reported for other northern Andean páramo ecosystems. In Chingaza National Natural Park, Colombia, and Sierra Nevada de Mérida National Park, Venezuela, white-tailed deer densities ranging from 11.55 to 43 ind km-² and groups of up to 50 individuals have previously been reported (Correa-Viana 1994; Mateus-Gutiérrez 2014). Similarly, in the ACH Antisana, groups of up to 49 white-tailed deer were recorded. However, more recent studies conducted in Chingaza National Natural Park revealed substantially lower densities, ranging from 0.16 to 2.09 ind km-² depending on the study sector and estimation method (Carrillo-Villamizar and López-Arévalo 2024). These authors also reported that deer abundance has declined compared to previous estimates from the same park, where the highest abundances were recorded in 2004, suggesting important temporal and spatial variation in population density within páramo ecosystems. Despite these lower current densities, previous studies in Chingaza documented the formation of large groups of white-tailed deer, which is consistent with the large aggregations observed in the ACH Antisana (Mateus-Gutiérrez 2014; Carrillo-Villamizar and López-Arévalo 2024).

At the local scale, however, no significant differences in deer densities were detected between different cover types (grassland, shrubland, and sandy area) within each year, nor when comparing the same cover type between 2023 and 2024 suggesting that cover type alone does not strongly influence their distribution and abundance in the study area (Pérez-Moreno et al. 2020). Nevertheless, this does not imply a homogeneous spatial distribution. White-tailed deer are often associated with heterogeneous environments, where different vegetation types may provide complementary resources, including forage and cover (Fulbright and Ortega 2007; Gastelum-Mendoza et al. 2023). In this context, suggesting that patterns of deer density are driven more by the integration of resources of the landscape than by individual cover types. In Mexico, López-Téllez et al. (2007) reported variations in deer density in four locations: Mitepec and El Salado exhibited high densities (3.2–3.4 ind km-²), while Huachinantla and Jolalpan exhibited low densities (0.1–0.5 ind km-²). This may be due to spatial and temporal components (availability of vegetation cover, food, and water), which affect the deer distribution patterns (Garavito 2004; Chávez et al. 2022).

In this context, variations in cover types of use may also be influenced by population density and perceived predation risk. Under high-density conditions, females with young and other individuals may prefer areas with dense vegetation that provide greater protection (Garavito 2004). Although differences were not statistically significant, higher density values were observed in shrubland and grassland cover types.

Shrublands likely provide both shelter and high-quality forage resources, as white-tailed deer are selective feeders that preferentially consume young leaves and stems of shrubs, which are rich in protein and low in fiber and lignin (Fulbright and Ortega 2006; Pérez-Moreno et al. 2020). In addition, woody vegetation often constitutes a major component of their diet, and increases in shrub availability have been linked to population growth in some regions (Taylor and Hahn 1947; Ramírez et al. 1996; Arceo et al. 2005). Also, their dense structure may reduce detectability and increase protection from predators (Christopher et al. 2002; Vargas and Pedraza 2004; Ortiz-Martínez et al. 2005).

Grasslands are dominated by grasses and herbaceous plants that serve as key complementary food resources, particularly under conditions of increased competition or seasonal variation in forage availability (Brokx and Andressen 1970; Gallina 1990; Gallina and Bello 2010; Plata et al. 2009, 2011). Previous studies have also reported that white-tailed deer use shrublands primarily for cover and grasslands for feeding and resting (Correa-Viana 1994). Together, these findings suggest that observed density patterns are better explained by the combined availability of resources across vegetation types than by any single cover type component.

Additionally, other cover types may fulfill complementary functions. Harley (2019) mentions that deer species such as red deer (Cervus elaphus) take dust baths using their antlers to dig into the ground. Although this behavior has not yet been documented in white-tailed deer, sandy areas may provide opportunities for similar behavioral activities, as patches of sand with multiple tracks were observed during fieldwork, as if the deer had rolled around there. These observations highlight the need for further studies to confirm the potential role of sandy areas in the species’ ecology.

The spatial distribution pattern observed in the ACH Antisana indicates a non-random aggregation of white-tailed deer within specific zones of the landscape. Higher densities concentrated in central and southwestern areas suggest the presence of core use areas within the protected landscape, while lower densities in peripheral zones may reflect reduced cover type suitability or higher levels of disturbance. Ungulate populations in heterogeneous and protected landscapes commonly exhibit core–periphery spatial structures, where individuals concentrate their activity in core areas that maximize access to resources while minimizing exposure to disturbance and perceived risk (Boyce et al. 2016). This pattern coincides with the spatial distribution of anthropogenic pressures in many protected area systems, which are often not uniformly distributed but tend to be concentrated near the edges, while central areas can function as relative refuges with lower levels of disturbance (Dillon et al. 2025). In this context, the spatial aggregation of deer over the two years may reveal a non-uniform distribution in the study area, with higher values concentrated in central zones and lower densities toward the periphery. This indicates that broader spatial processes, such as landscape structure and the distribution of resources or disturbances, may play a more important role than cover type per se in shaping deer distribution patterns.

One limitation of this study is that sampling was conducted only during the last half of 2023 and 2024, which restricts a more complete understanding of the species’ population dynamics throughout the year. According to López-Téllez et al. (2007), deer density estimates can vary between locations and transects depending on the sampling time. In this regard, it is worth noting that sampling took place during the transition between the dry and rainy seasons (October–December), a period in which changes in resource availability and individual activity may occur. Despite this temporal restriction, the study involved an extensive field phase, with a greater sampling effort than that carried out by Albuja (2007) and Tellkamp et al. (2019). To the best of our knowledge, there are no specific studies on the reproductive seasonality of the white-tailed deer in the ACH Antisana. Furthermore, the species’ reproduction in temperate and cold zones begins in late November, peaking in December (Albuja 2007; Ugarte 2011; Chávez et al. 2022). During this period, males and females increase their activity in searching for mates, which can increase their detectability (Hölzenbein and Marchinton 1992; Galindo-Leal and Weber 1998). In 2024, sampling was extended until December 11, partially coinciding with the reproductive season and the beginning of the rainy season, which could partially explain the increase in deer density recorded that year, although the differences were not statistically significant. In contrast, most of the 2023 sampling was conducted in October, during the dry season and outside the breeding season.

Although despite the annual estimates showed good accuracy, evidenced by their low coefficients of variation, which reflect less variability in the data and therefore greater sampling reliability (Shechtman 2013), the smaller number of observations in some cover types may have influenced the ability to detect differences. In distance sampling, small sample sizes can affect the accuracy of the detection function and increase the variability of density estimates (Buckland et al. 2015). Therefore, although no significant differences were identified, the presence of subtle variations between cover types cannot be ruled out.

Long-term monitoring programs are essential to better understand the population dynamics of white-tailed deer and establish management strategies tailored to the conditions of each territory (Sánchez-Rojas 2009; Abrante-Hernández et al. 2013). These studies should include not only information on the population density of the species but also on its age structure, growth rate, and sex ratio, as this information is key to understanding the dynamics of any animal population (Ezcurra and Gallina 1981; Mandujano and Aranda 1993; Ojasti and Dallmeier 2000). We recommend implementing systematic protocols for estimating white-tailed deer population densities based on direct observation methods, as these provide reliable information on the number of individuals and are a useful tool for establishing long-term monitoring programs.

Conclusions

This study provides updated evidence of relatively high densities of white-tailed deer in the ACH Antisana compared to historical records of the zone, suggesting a positive population trend that is likely associated with ongoing ecological recovery processes, including vegetation restoration, reduction of anthropogenic pressure, and improved cover type conditions.

No significant differences in deer densities were detected among cover types or between sampling years, indicating that cover type alone does not strongly determine deer density. Instead, the species appears to respond to a heterogeneous landscape where cover type elements provide complementary resources that jointly support population maintenance.

Overall, these results highlight that white-tailed deer density is closely linked to ecosystem recovery processes and site heterogeneity. Our findings highlight the importance of continuing restoration and management actions within the ACH Antisana, especially in areas that have been historically degraded by anthropogenic activities. The conservation of this high Andean ecosystem not only guarantees the survival of the white-tailed deer but also contributes to the preservation of other species that depend directly or indirectly on it and to maintaining ecosystem services. Thus, promoting comprehensive conservation strategies in the paramo are essential to ensure the long-term dynamics of the ecosystem.

Acknowledgements

We would like to thank the Ministry of Environment, Water, and Ecological Transition (MAATE) for the research permits: MAATE-ARSFC-٢٠٢٣-٣٣٣٨, MAATE-ARSFC-٢٠٢٤-٠٦٨٦. We would also like to thank the Andean Condor Foundation (FCA) for its financial and logistical support in the field. We also thank FONAG and EPMAPS for their logistical support during fieldwork, especially Braulio Lahuatte. We are especially grateful to Don Manuelito Simba and the Rodríguez family for their valuable advice, assistance in the field, and access to sites of interest. We also thank the anonymous reviewers for their valuable comments, which improved the content of this article. Ethical statement research permits were granted by the Ministerio de Medio Ambiente, Agua y Transición Ecológica (MAATE): MAATE-ARSFC-٢٠٢٣-٣٣٣٨, MAATE-ARSFC-٢٠٢٤-٠٦٨٦.

Declaration of Artificial Intelligence Use

We declared that we used DeepL Translate for a preliminary translation. Also, we used ChatGPT for brainstorming and outlining purposes.

Author contributions

Michelle Alexandra Rodríguez-Noroña: Conceptualization, fieldwork, data curation, formal analysis, fund acquisition, research, methodology, project administration, resources, software, visualization, original draft writing, writing-reviewing and editing; David Andrés Herrera-Orellana: Fieldwork, data curation, writing-reviewing and editing; Paúl Monar-Barragán: Conceptualization, writing-reviewing and editing, fund acquisition, research, project management, resources, validation, writing-reviewing and editing; Juan Sebastián Restrepo-Cardona: Validation, writing, revision, and editing; Evelyn Edith Araujo: Conceptualization, acquisition of funds, research; Iván Vinicio Jácome-Negrete: Conceptualization, fieldwork, supervision, research, resources, validation, writing-reviewing and editing.

Supplementary data

SD1. Direct observation of white-tailed deer in cover types transects in the Antisana Water Conservation Area in 2023 and 2024: A) Observation with rangefinder, B) male white-tailed deer, C) Observation with rangefinder, D) Group of white-tailed deer, E) Data entry on field form, F) female white-tailed deer

SD2. Summary of detection function models fitted to Deer data. “CvM” stands for Cramér-von Mises, Pa is average detectability, se(Pa) is standard error. Models are sorted in ascending order according to their AIC.

SD3. Adjustments to the detection function (Hazard-rate + cos) to the perpendicular distance distribution data for the years 2023 and 2024.

SD4. Database collected in 2023 used to estimate the density of white-tailed deer (O. virginianus ustus) using the R-Distance package.

SD5. Database collected in 2023 used to estimate the density of white-tailed deer (O. virginianus ustus) in cover types (TH: grassland, TB: shrubland, TE: sandy areas) using the R-Distance package.

SD6. Database collected in 2024 used to estimate the density of white-tailed deer (O. virginianus ustus) using the R-Distance package.

SD7. Database collected in 2024 used to estimate the density of white-tailed deer (O. virginianus ustus) in cover types (TH: grassland, TB: shrubland, TE: sandy areas) using the R-Distance package.

SD8. R scripts used for density estimates and construction of white-tailed deer figures.

Literature cited

Abrante-Hernández T, Hernández-Martínez F, and Ramírez-Medina L. 2013. Distribución y abundancia de Odocoileus virginianus (Venado de Cola Blanca) en la empresa forestal Integral Minas, Pinar del Río, Cuba. Revista Forestal Baracoa 32:21–27.

Adams H, Kissell R, Ratajczak D, Warr E, Applegate R, Barrett L, et al. 2020. Relationships among white-tailed deer density, harvest, and landscape metrics in TN, USA. European Journal of Wildlife Research 66:19. https://doi.org/10.1007/s10344-019-1353–8

Albuja L. 2007. Biología y ecología del Venado de cola blanca (Odocoileus virginianus ustus Gray, 1874) en los páramos de Oyacachi-Papallacta y Antisana, Ecuador. Politécnica ٢٧:٣٤–٥٧.

Arceo G, Mandujano S, Gallina S, and Pérez J. ٢٠٠٥. Diet diversity of white-tailed deer (Odocoileus virginianus) in a tropical dry forest in Mexico. Mammalia ٦٩:١٥٩–١٦٨. https://doi.org/١٠.١٥١٥/mamm.٢٠٠٥.٠١٤

Arenas P. ٢٠١١. El fotoperiodo y su relación con la reproducción del venado cola blanca (Odocoileus virginianus miquihuanensis) en el Altiplano Potosino. [Bachelor›s Thesis]. [Montesillo (MEX)]: El Colegio de Postgraduados.

Arroyo M, and Cavieres L. ٢٠١٣. High-Elevation Andean Ecosystems. Encyclopedia of Biodiversity ٤:٩٦–١١٠.

Báez S, Jaramillo L, Cuesta F, and Donoso D. ٢٠١٦. Effects of climate change on Andean biodiversity: a synthesis of studies published until ٢٠١٥. Neotropical Biodiversity ٢:١٨١–١٩٤.

Bécquer S. ٢٠٢٢. Análisis de bancos de semillas de cuatro usos de suelos del páramo del Área de Conservación Hídrica Antisana. [Bachelor›s Thesis]. [Quito (ECU)]: Universidad Central del Ecuador.

Beltrán C, and Díaz A. ٢٠١٠. Estimación de la densidad poblacional del venado cola blanca texano (Odocoileus virginianus texanus), introducido en la UMA “Ejido de Amanalco” Estado de México. CIENCIA Ergo-sum ١٧:١٥٤–١٥٨.

Boyce M, Johnson C, Merrill E, Nielsen S, Solberg E, and Van Moorter B. ٢٠١٦. Can habitat selection predict abundance? Journal of Animal Ecology ٨٥:١١–٢٠. https://doi.org/١٠.١١١١/١٣٦٥-٢٦٥٦.١٢٣٥٩

Brokx P, and Andressen F. ١٩٧٠. Análisis de contenidos estomacales del venado cara- merudo de los llanos venezolanos. Boletín de la Sociedad Venezolana de Ciencias Naturales ٢٨:٣٣٠–٣٥٣.

Buckland S, Rexstad E, Marquez T, and Oedekoven C. ٢٠١٥. Distance Sampling: Methods and Applications. New York (USA): Springer.

Carrillo-Villamizar J, and López-Arévalo H. ٢٠٢٤. Spatial variation of population density and age structure of White-tailed deer (Odocoileus virginianus) in a Colombian moor. Therya ١٥:٥٩–٦٩. https://doi.org/١٠.١٢٩٣٣/therya-٢٤-٥٤٢٥

Chávez D, Acosta N, and Andrade V. ٢٠٢٢. Manejo y cría del Venado de Cola Blanca (Odocoileus virginianus). ١st Edition. Santa Elena (ECU): Editorial Binario.

Christopher S, Jenks J, Griffin S, Rice L, and Higgins K. ٢٠٠٢. White-Tailed Deer habitats in the Central Black Hills. Journal of Range Management ٥٥:٢٤٢–٢٥٢.

Cincotta R, Wisnewski J, and Engelman R. ٢٠٠٠. Human population in the biodiversity hotspots. Nature ٤٠٤:٩٩٠–٩٩٢. https://doi.org/١٠.١٠٣٨/٣٥٠١٠١٠٥

Contreras-Moreno B, Hildalgo M, Hurtado R, López-González C, and De la Cruz A. ٢٠٢١. Seasonal home-range size of the white-tailed deer, Odocoileus virginianus thomasi, in a tropical wetland of southeastern Mexico. Revista Mexicana de Biodiversidad ٩٢: e٩٢٣٦٦٠. https://doi.org/١٠.٢٢٢٠١/ib.٢٠٠٧٨٧٠٦e.٢٠٢١.٩٢.٣٦٦٠

Correa-Viana M. ١٩٩٤. Actividad diaria y selección de hábitat por el venado caramerudo, Odocoileus virginianus, en Masaguaral, Edo. Guárico, Venezuela. BioLlania ١٠:٣٣-٤٢.

Crespo P, Celleri R, Buytaert W, Feyen J, Iñiguez V, Borja P, et al. ٢٠١٠. Land use change impacts on the hydrology of wet Andean paramo ecosystems. Status and Perspectives of Hydrology in Small Basins ٣٣:٧١–٧٦. https://doi.org/١٠.١٣١٤٠/٢.١.٥١٣٧.٦٣٢٠

Dillon B, Possingham H, and Holden M. ٢٠٢٥. Expand or better manage protected areas: a framework for minimizing extinction risk when threats are concentrated near edges. Biological Conservation ٣١١:١١١٤٦٩. https://doi.org/١٠.١٠١٦/j.biocon.٢٠٢٥.١١١٤٦٩

Etter A, Andrade A, Saavedra-Ramírez K, Amaya P, Arévalo P, Cortes J, et al. ٢٠١٧. Lista roja de ecosistemas de Colombia. Vol. ٢. Bogotá (COL): Pontificia Universidad Javeriana and Conservación Internacional Colombia.

Ezcurra E, and Gallina S. ١٩٨١. Biology and population dynamics of white-tailed deer in northwestern Mexico. In: Ffolliott PS, and Gallina S, editors. Deer biology, habitat requirements and management in western North America. Xalapa (MEX): Instituto de Ecología, México; p.٧٧–١٠٨.

FONAG. ٢٠١٧. Programa de Recuperación de la Cobertura Vegetal: contratar la implementación de parcelas experimentales en zonas altamente degradadas en el Área de Conservación Hídrica Antisana (ACHA). [Accessed February ٢٨th, ٢٠٢٤]. https://www.fonag.org.ec/web/wp-content/uploads/٢٠١٧/٠٩/TDR-Parcelas-experimentales-Antisana.pdf

FONAG. ٢٠١٩. Portafolio de intervenciones: Reposición de Huella Hídrica. [Accessed July ٦th, ٢٠٢٣]. https://www.fonag.org.ec/web/wp-content/uploads/٢٠١٩/٠٩/٢٤-min.pdf

FONAG. ٢٠٢١. Informe de cumplimientos de metas ٢٠٢١. [Accessed February ٢٨th, ٢٠٢٤]. https://www.fonag.org.ec/web/wp-content/uploads/٢٠٢٣/٠٣/INFORME-DE-CUMPLIMIENTO-METAS-FONAG_٢٠٢١_compressed.pdf

FONAG. ٢٠٢٤. Portafolio de intervenciones: Reposición de huella hídrica. [Accessed July ٢nd, ٢٠٢٥]. https://www.fonag.org.ec/web/wp-content/uploads/٢٠٢٤/٠٧/Portafolio-de-Intervenciones-Huella-Hidrica.pdf

Fulbright T, and Ortega J. ٢٠٠٦. White-tailed deer habitat: ecology and management in rangelands. College Station (USA): Texas A&M University Press.

Fulbright T, and Ortega J. ٢٠٠٧. Ecología y Manejo del venado de cola blanca. College Station (USA): Texas A&M University Press.

Galindo-Leal C. ١٩٩٢. Overestimation of deer densities in Michilia Biosphere Reserve. Southwestern Naturalist ٣٧:٢٠٩–٢١٢. https://doi.org/١٠.٢٣٠٧/٣٦٧١٦٧٣

Galindo-Leal C, and Weber M. ١٩٩٨. El venado de la Sierra Madre Occidental. Ecología, manejo y conservación. ١st Edition. Mexico D.F. (MEX): Ediciones Culturales.

Gallina S. ١٩٩٠. El venado cola blanca y su hábitat en la Michilia, Durango. [PhD thesis]. [México D.F. (MEX)]: Universidad Nacional Autónoma de México.

Gallina S, and Bello J. ٢٠١٠. El gasto energético del venado cola blanca (Odocoileus virginianus texanus) en relación a la precipitación en una zona semiárida de México. Therya ١:٩–٢٢.

Gallina S, Mandujano S, and Villarreal O. ٢٠١٤. Monitoreo y manejo del Venado de Cola Blanca “Conceptos y Métodos”. Veracruz (MEX): Instituto de Ecología, A. C. and Benemérita Universidad Autónoma de Puebla.

Garavito A. ٢٠٠٤. Caracterización y uso del hábitat del venado de cola blanca (Odocoileus virginianus) en la Reserva Forestal Protectora del Río Blanco (Cundinamarca-Colombia). [Bachelor›s Thesis]. [Bogotá (COL)]: Pontificia Universidad Javeriana.

Gastelum-Mendoza F. ٢٠٢٣. Forage diversity and selection in white-tailed deer (Odocoileus virginianus Texanus MEARNS) in Coahuila, Mexico: Forage selection of white-tailed deer in Coahuila. Agro Productividad. ١٦:٩٧–١٠٧. https://doi.org/١٠.٣٢٨٥٤/agrop.v١٦i١.٢٣٦٣

Grubb P, Lloyd J, Pennington T, and Páez-Bimos S. ٢٠٢٠. A historical baseline study of the páramo of Antisana in the Ecuadorian Andes including the impacts of burning, grazing and trampling. Plant Ecology and Diversity ١٣:٢٢٥–٢٥٦. https://doi.org/١٠.١٠٨٠/١٧٥٥٠٨٧٤.٢٠٢٠.١٨١٩٤٦٤

Hansen L, Flinn E, Summers J, Gao X, and Millspaugh J. ٢٠١٧. Effects of an antler point restriction on harvest of White-Tailed Deer in Missouri. Wildlife Society Bulletin ٤١:٥١٦–٥٢٢. https://doi.org/١٠.١٠٠٢/wsb.٧٨٩

Harley C. ٢٠١٩. Water use behaviors and effect on standing water quality by axis deer (Axis axis) in the Texas Hill Country. [Master›s thesis]. [Texas (USA)]: Universidad Texas.

Hewitt D. ٢٠١١. Biology and Management of White-tailed Deer. ٤th Edition. Florida (USA): CRC Press.

Hofstede R, and Mujica E. ٢٠٠٢. Birth of the Páramo Group. An international network of people, institutions, and projects working on páramo. Mountain Research and Development ٢٢:٨٣–٨٤. http://dx.doi.org/١٠.١٦٥٩/٠٢٧٦-٤٧٤١(٢٠٠٢)٠٢٢[٠٠٨٣:BOTPRG]٢.٠.CO;٢

Hölzenbein S, and Marchinton L. ١٩٩٢. Spatial integration of maturing male white-tailed deer into the adult population. Journal of Mammalogy ٧٣:٣٢٦–٣٣٤. https://doi.org/١٠.٢٣٠٧/١٣٨٢٠٦٥

Lambertucci S, Trejo A, Di Martino S, Sánchez-Zapata J, Donázar J, and Hiraldo F. ٢٠٠٩. Spatial and temporal patterns in the diet of the Andean condor: Ecological replacement of native fauna by exotic species. Animal Conservation ١٢:٣٣٨–٣٤٥. https://doi.org/١٠.١١١١/j.١٤٦٩-١٧٩٥.٢٠٠٩.٠٠٢٥٨.x

LaRue M, Nielsen C, and Grund M. ٢٠٠٧. Using distance sampling to estimate densities of white-tailed deer in south-central Minnesota. The Prairie Naturalist ٣٩:٥٧-٦٨.

López-Téllez M, Mandujano S, and Yánes G. ٢٠٠٧. Evaluación poblacional del Venado Cola Blanca en un bosque tropical seco de la mixteca poblana. Acta Zoológica Mexicana ٣:١–١٦.

MAE. ٢٠١٣. Sistema de Clasificación de los Ecosistemas del Ecuador Continental. Quito (ECU): Subsecretaría de Patrimonio Natural.

Mandujano S. ٢٠١٤. Manual para estimar la densidad de venados en UMAs y ANPs empleando PELLET. ١st Edition. Xalapa (MEX): Instituto de Ecología A.C.

Mandujano S. ٢٠١٦. Venado Cola Blanca en Oaxaca. Potencial, conservación, manejo y monitoreo. ١st Edition. Xalapa (MEX): Instituto de Ecología A.C.

Mandujano S, and Aranda M. ١٩٩٣. Conteo de venados (Odocoileus virginianus: Cervidae) en transectos: recomendaciones para su aplicación. BIOTAM ٥:٤٣–٤٦.

Mandujano S, Pérez T, Escobedo L, Yánez C, Gonzáles A, Pérez L, et al. ٢٠١٠. Venados: Animales de los Dioses. ١st Edition. Xalapa (MEX): Instituto de Ecología A.C.

Martínez D, Albín J, Cabaleiro J, Pena T, Rivera F, and Blanco V. ٢٠٠٩. El Criterio de Información de Akaike en la Obtención de Modelos Estadísticos de Rendimiento. XX Jornadas de Paralelismo ١:٤٣٩–٤٤٤.

Mateus-Gutiérrez C. ٢٠١٤. Efecto de la estructura del hábitat sobre las características demográficas de dos poblaciones locales de venado de cola blanca, Odocoileus virginianus goudotii, en el Parque Nacional Natural Chingaza (Colombia). [Bachelor›s Thesis]. [Bogotá (COL)]: Universidad Nacional de Colombia.

Miller D, Rexstad E, Thomas L, Marshall L, and Laake J. ٢٠١٩. Distance Sampling in R. Journal of Statistical Software ٨٩:١–٢٨. https://doi.org/١٠.١٨٦٣٧/jss.v٠٨٩.i٠١

Myers N, Mittermeier R, Mittermeier C, Da Fonseca G, and Kent J. ٢٠٠٠. Biodiversity hotspots for conservation priorities. Nature. ٤٠٣:٨٥٣–٨٥٨. https://doi.org/١٠.١٠٣٨/٣٥٠٠٢٥٠١

Narváez V, and Zapata-Ríos G. ٢٠٢٠. Manual para el muestreo de fauna silvestre con transectos lineales. ١st Edition. Quito (ECU): Wildlife Conservation Society.

Navarro E. ٢٠٠٥. Abundancia relativa y distribución de los indicios de las especies de mamíferos medianos en dos coberturas vegetales en el Santuario de Flora y Fauna Otún Quimbaya Pereira-Colombia. [Bachelor›s Thesis]. [Bogotá (COL)]: Pontificia Universidad Javeriana.

Ojasti J, and Dallmeier F. ٢٠٠٠. Manejo de fauna silvestre neotropical. Washington, D.C. (USA): Smithsonian Institution MAB Biodiversity Program.

Ortiz-Martínez T, Gallina S, Briones-Salas M, and Gonzáles G. ٢٠٠٥. Densidad poblacional y caracterización del hábitat del Venado Cola Blanca (Odocoileus virginianus oaxacensis, Goldman y Kellog, ١٩٤٠) en un bosque templado de la Sierra Norte de Oaxaca, México. Acta Zoológica Mexicana ٢١:٦٥–٧٨.

Pérez-Mejía S, Mandujano S, and Martínez-Romero L. ٢٠٠٤. Tasa de defecación del venado cola blanca, Odocoileus virginianus mexicanus, en cautividad en Puebla, México. Acta Zoológica Mexicana ٢٠:١٦٧–١٧٠.

Pérez-Moreno H, Montenegro O, López-Arévalo H, and Sarmiento C. ٢٠٢٠. Densidad poblacional y uso de hábitat del venado cola (Odocoileus virginianus) durante la época de lluvias en la orinoquía colombiana. In López-Arevalo HF, and González-Hernández A. Ecología, uso, manejo y conservación del venado de cola blanca en Colombia. ١st Edition. Bogotá (COL): Proceditor Ltda.

Pérez E, and Zapata E. ٢٠٢٥. Variabilidad hidrometeoroló-gica del ecosistema del páramo: análisis temporal y comparativo de variables como la precipitación, presión atmosférica y humedad relativa del aire dentro del área de conservación hídrica Antisana. [Bachelor Thesis]. Quito (ECU): Universidad Politécnica Nacional.

Perrig P, Donadio E, Middleton A, and Pauli J. ٢٠١٧. Puma predation subsidizes an obligate scavenger in the high Andes. Journal of Applied Ecology ٥٤:٨٤٦–٨٥٣. https://doi.org/١٠.١١١١/١٣٦٥-٢٦٦٤.١٢٨٠٢

Piña E, and Trejo I. ٢٠١٤. Densidad poblacional y carac-terización de hábitat del Venado Cola Blanca en un bosque templado de Oaxaca, México. Acta Zoológica Mexicana ٣٠:١١٤–١٣٤.

Plata F, Ebergeny S, Resendiz J, Villarreal O, Barcena R, Viccone J, et al. ٢٠٠٩. Palatabilidad y composición química de alimentos consumidos en cautiverio por el venado cola blanca de Yucatán (Odocoileus virginianus yucatanensis). Archivos de Medicina Veterinaria ٤١:١٢٣–١٢٩.

Plata F, Mendoza G, Viccon J, Bárcena R, and Clemente F. ٢٠١١. Comparación de métodos basados en los requerimientos nutricionales y disponibilidad de biomasa para estimar la capacidad de carga para venado cola blanca. Archivos de Medicina Veterinaria ٤٣:٤١-٥٠.

QGIS Development Team. ٢٠١٩. QGIS Geographic Information System (Version ٣.١٠.١٣-A Coruña). Open-Source Geospatial Foundation. [Accessed August ٦th, ٢٠٢٣]. http://www.qgis.org

QGIS Project. ٢٠٢٠. QGIS ٣.١٠ User Guide. [Accessed August ٦th, ٢٠٢٣]. https://docs.qgis.org/٣.١٠/pdf/es/QGIS-٣.١٠-UserGuide-es.pdf

R Core Team. ٢٠٢١. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [Accessed June ٨th, ٢٠٢٣]. https://www.r-project.org

Ramírez R, Haenlein G, Treviño A, and Reyna J. ١٩٩٦. Nutrient and mineral profile of white-tailed deer (Odocoileus virginianus texanus) diets in northeastern Mexico. Small Ruminant Research ٢٣:٧–١٦. https://doi.org/١٠.١٠١٦/S٠٩٢١-٤٤٨٨(٩٦)٠٠٨٩٥-٤

Restrepo-Cardona JS, Monar-Barragán P, Orihuela-Torres A, Araujo EE, Narváez F, Pineida R, et al. ٢٠٢٥. New records of domestic dog negative impacts on native vertebrates in the Ecuadorian Andes. Neotropical Biology and Conservation ٢٠:٩٣–١٠١. https://doi.org/١٠.٣٨٩٧/neotropical.٢٠.e١٤٨٢٢١

Restrepo-Cardona JS, Parrado M, Vargas F, Kohn S, Sáenz-Jiménez F, Potaufeu Y, et al. ٢٠٢٢. Anthropogenic threats to the Vulnerable Andean Condor in northern South America. Plos ONE ١٧:e٠٢٧٨٣٣١. https://doi.org/١٠.١٣٧١/journal.pone.٠٢٧٨٣٣١

Rojas L. ٢٠١٠. Evaluación del uso y calidad del hábitat en poblaciones del Venado de Cola Blanca (Odocoileus virginianus) en la Reserva Natural La Aurora, Municipio de Hato Corozal, Casanare. [Bachelor Thesis]. [Bogotá (COL)]: Pontificia Universidad Javeriana.

Saalfeld S, Ditchkoff S, Ozoga J, and Mitchell M. ٢٠٠٨. Seasonal Variation in Sex Ratios Provides Developmental Advantages in White-tailed Deer, Odocoileus virginianus. The Canadian Field-Naturalist ١٢٢:٨–١٤.

Sánchez B. ٢٠٠٩. El Eco Parque Andino del Complejo Recrecional El Porvenir aporta en la conservación del Venado de Cola Blanca. [Bachelor Thesis]. [Ambato (ECU)]: Universidad Técnica de Ambato.

Sánchez-Rojas G, Aguilar-Miguel C, and Hernández-Cid E. ٢٠٠٩. Estudio poblacional y uso de hábitat por el venado cola blanca (Odocoileus virginianus) en un bosque templado de la Sierra de Pachuca, Hidalgo, México. Tropical Conservation Science ٢:٢٠٤–٢١٤. https://doi.org/١٠.١١٧٧/١٩٤٠٠٨٢٩٠٩٠٠٢٠٠٢٠٧

Shechtman O. ٢٠١٣. The coefficient of variation as an index of measurement reliability. In: Doi S, and Williams G, editors. Methods of Clinical Epidemiology. Heidelberg (DEU): Springer; p. ٣٩–٤٩. https://doi.org/١٠.١٠٠٧/٩٧٨-٣-٦٤٢-٣٧١٣١-٨_٤

Sierra R, Calva O, and Guevara A. ٢٠٢١. La Deforestación en el Ecuador, ١٩٩٠–٢٠١٨. Factores promotores y tendencias recientes. Quito (ECU): Ministerio de Ambiente, Agua y Transición Ecológica del Ecuador, Ministerio de Agricultura y Ganadería del Ecuador.

Sklenář P, Kucerova A, Mackova J, and Macek P. ٢٠١٥. Temporal variation of climate in the high elevation paramo of Antisana, Ecuador. Geografia Fisica e Dinamica Quaternaria ٣٨:٦٧–٧٨. https://doi.org/١٠.٤٤٦١/GFDQ.٢٠١٥.٣٨.٠٧

Tellkamp M, Herrera H, Ponsot E, and Bautista I. ٢٠١٩. Population estimates and habitat selection of white-tailed deer (Odocoileus virginianus ustus) in the Antisana Water Conservation Area, Ecuador. Bionatura ٢:A١. https://doi.org/١٠.٢١٩٣١/CS/٢٠١٩.٠٢.٠١.٢٩

Terán-Valdez A, Duarte N, Cuesta F, and Pinto E. ٢٠١٨. Practicing ecological restoration: monitoring and maintenance of areas. In: Proaño R, Duarte N, Cuesta F, and Maldonado G, editors. Guía para la restauración de bosques montanos tropicales. Quito (ECU): CONDESAN; p. ٧٥.

Thomas L, Buckland S, Rexstad E, Laake J, Strindberg S, Hedley S, et al. ٢٠١٠. Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology ٤٧:٥–١٤. https://doi.org/١٠.١١١١/j.١٣٦٥-٢٦٦٤.٢٠٠٩.٠١٧٣٧.x

Ugarte Z. ٢٠١١. Estimación poblacional de venado cola blanca (Odocoileus virginianus miquihuanensis) en dos condiciones de vegetación del predio presa de San Antonio, municipio de Parras de la Fuente, Coahuila. [Bachelor Thesis]. [Saltillo (MEX)]: Universidad Autónoma Agraria “Antonio Narro”.

Urbanek R, and Nielsen C. ٢٠١٢. Comparison of aerial surveys and pellet-based distance sampling methods for estimating deer density. Wildlife Society Bulletin ٣٦:١٠٠–١٠٦. https://doi.org/١٠.١٠٠٢/wsb.١١٦

Vargas O, and Pedraza P. ٢٠٠٤. Parque Nacional Natural Chingaza. ١st Edition. Bogotá (COL): Gente Nueva Editorial.

Zapata-Ríos G, and Branch LC. ٢٠١٨. Mammalian carnivore occupancy is inversely related to presence of domestic dogs in the high Andes of Ecuador. PLoS ONE ١٣:e٠١٩٢٣٤٦. https://doi.org/١٠.١٣٧١/journal.pone.٠١٩٢٣٤٦

Associated editor: Rafael Reyna Hurtado

Submitted: November ٥, ٢٠٢٥; Reviewed: March ١٢, ٢٠٢٦

Accepted: June ٢, ٢٠٢٦; Published on line: July ٣, ٢٠٢٦

THERYA, 2026, Vol. 17(3):XXX-XXX

DOI: 10.12933/therya.2026.6250 ISSN 2007-3364

Figure ١. Geographic location of the ACH Antisana, Ecuador, where white-tailed deer data were collected during ٢٠٢٣ and ٢٠٢٤.

Figure ٢. Cover types (grassland, shrubland, and sandy area) and location of ٢٧ transects in grassland (١٨ transects), shrubland (٦ transects), and sandy areas (٣ transects) for the study of white-tailed deer in the ACH Antisana during ٢٠٢٣ and ٢٠٢٤.

Figure ٣. Selected cover types in the Área de Conservación Hídrica Antisana in ٢٠٢٣ and ٢٠٢٤: A) grassland, B) grassland transect, C) sandy areas, D) sandy areas transect, E) shrubland, F) shrubland transect.

Figure ٤. Quantile-quantile plots (Q-Q plots) indicating the fit of the detection function to the data for the years 2023 and 2024.

Figure 5. Box plot of white-tailed deer population density in the ACH Antisana transects for the years 2023 and 2024. Wilcoxon test for paired data, p > 0.05.

Table 1. White-Tailed Deer Population Density for 2023 and 2024 in the ACH Antisana

Parameters	Year 2023	Year 2024
Sampling Effort (km)	45	43
Number of Deer Groups Observed	161	92
Groups Density (groups km-2)	3.19 ± 0.26 (CV= 8.01%)	2.98 ± 0.35 (CV= 11.83 %)
Individuals Density (ind km-2)	12.61 ± 0.8 (CV= 6.03%)	14.25 ± 1.5 (CV= 10.41%)
Expected Group Size (ind group-1)	3.95 ± ٠.27 (CV= 6.82%)	4.79 ± ٠.33 (CV= 6.94%)
Estimated Abundance/Population (N)	1061.92 ± ٦٤.08 (CV= 6.03 %)	1200.66 ± 125.05 (CV= 10.41%)

N: Estimated deer population over 84.24 km2 (individual density times total area)

CV: Coefficient of variation

Table 2. White-Tailed Deer Population Density by cover types for 2023 and 2024 in the ACH Antisana

Cover types	2023			2024
	Observed Abundance	Estimated Abundance (N)	Density (ind km-2)	Observed Abundance	Estimated Abundance (N)	Density (ind km-2)
shrubland	91	125.09 ± 71.2 (CV= 56.92%)	8.04 ± 4.35 (CV= 54.1 %)	58	120.90 ± 71.03 (CV= 58.75%)	13.59± 7.99 (CV= 58.75%)
sandy areas	26	19.54 ± 10.57 (CV= 54.10%)	8.04 ± 4.35 (CV= 54.10 %)	13	14.82 ± 4.37 (CV= 29.49%)	6.09 ± 1.80 (CV= 29.49%)
grassland	500	939.2 ± 219.16 (CV= 23.33%)	12.88 ± 3.01 (CV= 23.33%)	399	1203.33 ± 435.57 (CV= 36.20%)	16.50 ± 5.97 (CV= 16.20%)

N: Number of deer in each cover type per year, calculated from the density and the corresponding area (grassland: 72.92 km², shrubland: 8.89 km², sandy areas: 2.43 km²).

CV: Coefficient of variation

Figure 6. Population density of white-tailed deer in grassland, shrubland, and sandy areas in 2023 and 2024 in the ACH Antisana. Wilcoxon paired test p > 0.05 for shrubland, sandy areas, and grassland cover types.

Figure 7. Population density of white-tailed deer in grassland, shrubland, and sandy areas of the ACH Antisana in 2023. Kruskal Wallis test p >0.05.

Figure 8. Population density of white-tailed deer in grassland, shrubland, and sandy areas in the Antisana Water Conservation Area in 2024. Kruskal-Wallis test p >0.05.

Figure 9. Heat maps of white-tailed deer population density in the grassland, shrubland, and sandy areas of the Antisana Water Conservation Area in 2023 and 2024. Warm colors indicate areas of higher population density, while cool colors represent areas of lower density. Density measured in ind km-2

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