Linking Thermal Availability to Mammal Activity Patterns through Long-Term Camera Trap Monitoring in the Southern Andean Yungas

Linking Thermal Availability to Mammal Activity Patterns through Long-Term Camera Trap Monitoring in the Southern Andean Yungas | 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 Linking Thermal Availability to Mammal Activity Patterns through Long-Term Camera Trap Monitoring in the Southern Andean Yungas Sebastián Alejandro Albanesi, Daniel Andrés Dos Santos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9369097/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study investigates the role of environmental temperature as a primary driver of mammalian activity patterns in the Southern Andean Yungas, utilizing a robust 14-year camera trap dataset (2009–2023) comprising 573 stations and over 20,000 trap-days. By focusing on detection events of ten focal species alongside hourly temperature data, we find that mammal activity is strongly concentrated within a moderate thermal window of 11–21°C. This ecological core also embraces the optimal window for the majority of the recorded assemblage. Species exhibit considerable behavioral plasticity, temporally shifting their daily activity to track this thermal niche across seasons: during warm months, activity increases during nocturnal and crepuscular periods to avoid thermal stress, while in cooler months, activity becomes more concentrated in diurnal hours. Furthermore, we found that the landscape matrix mediates these responses; while forest populations show marked seasonal shifts (bimodal in summer, unimodal in winter), populations in productive agricultural areas maintain a bimodal pattern year-round. This suggests that anthropogenic habitats alter the expression of thermoregulatory behaviors. Our results highlight a complex interplay between temperature and time, where species-specific responses—ranging from the nocturnal shift of the Lowland Tapir ( Tapirus terrestris ) to the crepuscular adjustments of the Crab-eating Fox ( Cerdocyon thous )—facilitate spatio-temporal niche segregation. These findings establish thermal availability as a fundamental structuring force of temporal ecology in subtropical montane systems, providing critical insights for forecasting species resilience under climate change and habitat fragmentation. Mammalian activity Thermal window Behavioral plasticity Yungas Camera trapping Spatio-temporal segregation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Mammals are endothermic animals, capable of generating their own body heat to maintain vital functions without directly depending on environmental conditions. However, temperature, natural light cycles and human activities play a fundamental role in their physiology and behavior. Many mammals exhibit daily activity rhythms influenced by changes in ambient temperature, suggesting that this factor is key to patterns of speciation and diversification in mammals among other vertebrates. In the face of climate change and habitat alteration, understanding species’ thermal niche breadth is critical for developing conservation strategies and identifying priority areas for protection. While organisms can respond to environmental shifts through phenotypic plasticity, dispersal, or genetic adaptation, the latter two are often constrained under rapid change. Dispersal may be limited by habitat fragmentation, and genetic adaptation can be too slow for long-lived species. Phenotypic plasticity—encompassing morphological, physiological, and behavioral changes—thus becomes a key response (Piersma & Drent, 2003 ). Behavioral adjustments, in particular, can occur quickly and reversibly, making them especially relevant under abrupt environmental shifts such as the recent rise in global temperatures (Van Buskirk, 2012 ). In warm environments, nighttime can serve as a thermal refuge for many mammals, helping them avoid the extreme heat of the day. Shifting activity to cooler periods—especially at night—is a widespread behavioral strategy to cope with rising temperatures (Berry et al., 2023 ; Hetem et al., 2012 ; McFarland et al., 2014 ). Indeed, most mammals are nocturnal, accounting for 69% of species in the global dataset analyzed by Bennie et al. ( 2014 ), compared to 20% diurnal, 8.5% cathemeral, and 2.5% crepuscular. Nocturnality not only mitigates thermal stress but also reduces the likelihood of encounters with humans, acting as either a facultative or obligate response. Bennie et al. ( 2014 ) show that diel activity patterns—functional traits shaped by physiological and morphological adaptations—are strongly influenced by climate and the availability of biologically useful daylight. Diurnal activity tends to be more common in regions with cold nights, where the energetic cost of nocturnality is high, while nocturnal species dominate in arid and warm regions. In the Neotropics, including the Yungas, nocturnal activity patterns are also prevalent among mammals (Albanesi et al., 2016 ), underscoring the dual adaptive value of nighttime activity in response to both environmental conditions and anthropogenic pressures. These temporal strategies reflect the interplay between thermal constraints, light environments, and increasing human disturbance, all of which shape species’ current and potentially future distributions. The use of camera traps in wildlife research has surged since the early 2000s (Burton et al., 2015 ), offering a non-invasive and efficient tool to monitor elusive species and remote areas (Rovero & Zimmermann, 2016 ). These devices are now widely applied in terrestrial vertebrate studies for purposes ranging from species inventories and population density estimates to behavior and movement analyses (Tobler et al., 2008 ; Gilbert et al., 2021 ; Niedballa et al., 2019 ). Recent advances in camera trap technology have enabled rapid and systematic monitoring of terrestrial vertebrate communities, particularly medium and large bodied species, and have opened new avenues for regional and global scale ecological research (Steenweg et al., 2017 ). Although most studies remain site specific, a growing number are pooling data across networks to address broader conservation and biogeographic questions (Beaudrot et al., 2016 ; Davis et al., 2018 ; Rovero et al., 2020 ), emphasizing the potential of camera trap datasets to inform global assessments. In our study, we take advantage of a 14 year continuous dataset, professionally vetted for species identification, to explore how mammalian activity patterns are structured in relation to temperature as well as ecological features of landscapes. By incorporating an underused variable—ambient temperature recorded at each detection event—we aim to delineate the thermal activity niche of the mammalian assemblage in a productive landscape of the Yungas ecoregion. Beyond documenting species presence and activity, our approach leverages the positive detection data—that is, instances where individuals were actually recorded—to estimate the thermal preferences of each species. We treat these temperature-associated records as behavioral evidence of realized thermal niches, assuming that animals are more likely to be active under favorable thermal conditions. Based on this, we delineate species-specific thermal preference ranges from observed data. We then assess how the availability of these preferred thermal conditions varies across the daily cycle throughout different months of the year, effectively quantifying the temporal offer of thermally optimal windows. This framework allows us to examine whether and how species adjust their activity patterns seasonally in response to the shifting availability of their thermal optima. Crucially, this analysis includes species with different diel habits—diurnal, nocturnal, cathemeral, crepuscular—revealing instances where individuals diverge from their usual temporal niches to take advantage of more favorable thermal windows. Such flexibility, or lack thereof, may have significant implications for species’ resilience under warming conditions. To understand how thermal conditions shape mammal activity patterns in subtropical montane forests, we used a 14-year camera trap dataset to estimate species-specific thermal preferences based on positive detection events. We then assessed the daily and seasonal availability of these preferred temperature ranges and evaluated how closely species' activity aligns with this thermal niche across regions (Northern vs. Southern Yungas), months, and landscape contexts (Forest vs. Productive). We hypothesize that cathemeral species will exhibit a crepuscular pattern aligning with thermally favorable dawn and dusk periods, modulated by season; specifically, they will be more active at night during summer and during the day in winter. Nocturnal species are expected to concentrate activity during summer nights, while in winter, they may reduce nocturnality, shifting activity toward twilight or even daytime hours to track their thermal optimum. Conversely, diurnal species will shift activity toward crepuscular hours during hot months to avoid thermal stress. Ultimately, this framework will help identify robust indicator species for monitoring mammal responses to environmental change. Materials and methods Study area The Southern Andean Yungas ecoregion is a montane forest system that stretches across the departments of Santa Cruz, Chuquisaca, and Tarija in southern Bolivia, and through the provinces of Salta, Jujuy, Tucumán and Catamarca in northwestern Argentina, spanning a latitudinal gradient between 22° and 29°S. The Yungas forest system is distributed along mountain slopes over an altitudinal gradient ranging from 400 to 2,500 meters above sea level (Brown et al., 2001 ). Most camera trap records are concentrated in the piedmont zone below 700 meters above sea level. Along this elevation gradient, vegetation is organized into altitudinal belts, each with distinct physiognomic characteristics (Brown et al., 2001 ). The Southern Yungas host the greatest biodiversity in Argentina, granting them high conservation value. Additionally, they play a critical hydrological role, as most rivers that supply key urban centers and agricultural areas in the region originate in these forests (Brown et al., 2001 ). Precipitation in the Southern Yungas follows a seasonal pattern, with 75–80% of rainfall occurring during the summer months, from November to March (Minetti, 2005 ). During the dry season, from May to August, frequent mist events partially compensate for the lack of rainfall, which is why these forests are often referred to as “cloud forests” (Brown et al., 2001 ). The annual mean temperature ranges from 20.2°C in the northern portion of the ecoregion to 14.8°C in the southern sectors, with transitional zones near the Chaco region reaching up to 23.8°C. Some sites in the Yungas experience an annual thermal amplitude of up to 8°C, with August showing the greatest variation. Spring temperatures tend to be higher than those in autumn. Absolute minimum temperatures can reach − 3°C to − 4°C, with the first frosts typically occurring in June or July, and no frosts recorded after mid-September. Temperature is strongly and inversely correlated with elevation, and additionally, slope aspect has a local effect on both solar radiation and site humidity. The Yungas, like the Paraná Forest, are characterized by high levels of forest exploitation, hunting, subsistence livestock farming, and commercial ranching. These activities vary along the marked altitudinal and latitudinal gradients present in the region (Nanni et al., 2020 ). Camera trapping and data acquisition We worked with a database comprising 44 independent surveys conducted between 2009 and 2023 in various habitats, including continuous and riparian forests, as well as plantations of sugarcane, lemon, and forestry, depending on the objective of each survey. A total of 573 camera traps were deployed (Fig. 1 ), with a cumulative effort of 20,871.45 camera trap days. The surveys took place across three provinces within the Yungas ecoregion of Argentina. Sampling coverage showed a decreasing gradient from Jujuy (344 cameras, 12,312.34 camera trap days) to Salta (44 cameras, 1,806.42 camera trap days), with Tucumán occupying an intermediate position (185 cameras, 6,752.69 camera trap days). Sampling designs Species were recorded using camera traps of various brands and models (Moultrie DGS-M40, Bushnell Trophy Cam HD, Bushnell Trophy Cam HD Aggressor, Bushnell Prime, Bushnell Prime No Glow, Bushnell Core DS-4K). Each sampling station consisted of a single camera aimed at bait (a can of tuna) placed approximately 3 meters away, located off trails and paths, at a height of 50 cm above the ground. Cameras were programmed to take three consecutive photographs with a 5 minute interval between photo groups. The cameras also recorded temperature data. For this study, we used all species records but considered only one photo per group of three. Species selection and temporal activity analysis Our study centered on ten mammal species, which together comprised the vast majority of detection events and encompassed a variety of body sizes and trophic guilds. For each independent detection event, the time of capture was extracted and assigned to one of eight 3-hour intervals: [0,3), [3,6), [6,9), [9,12), [12,15), [15,18), [18,21) and [21,24). Each record was also classified according to the season: Dry/Temperate (April–September) and Rainy/Warm (October–March). For each species, we built contingency tables summarizing the frequency of detection events across hourly intervals and seasons. A chi-squared test of homogeneity (χ²) was applied to assess whether daily activity distributions differed significantly between seasons. Standardized residuals were inspected to identify specific time intervals contributing most to seasonal variation. Daily activity patterns were visualized using radar charts (8 sectors corresponding to the 3-hour intervals) that display the relative frequency of detections for each season. The resulting paired polygons (Dry/Temperate vs. Rainy/Warm) provide a direct graphical representation of potential shifts in activity intensity and timing. Radar charts were produced in R 4.3.3 (R Core Team, 2024 ) using the fmsb package. Significant seasonal differences (p < 0.05) were interpreted as evidence of behavioral adjustments to thermal conditions, particularly when species shifted activity from diurnal to nocturnal or crepuscular periods during warmer months. The relationship between average environmental temperature and temperature at detection events was examined using a two-dimensional kernel density estimation (2D kde), from which cumulative probability contours were derived to identify areas of highest observation density. To summarize the main structure of this relationship, we overlaid an inverted Z-shaped piecewise linear model on the density surface. The lower and upper bounds of the trapezoid were identified through an exhaustive search procedure, selecting the combination of breakpoints that minimized the mean squared error relative to the observed values. This simple descriptive model captures the alignment with the identity line within the thermoneutral range and the tendency for detection temperatures to converge toward its lower and upper bounds at the extremes of the environmental gradient. The trapezoidal fit is intended as a parsimonious approximation of the dominant pattern, which may in reality follow a smoother nonlinear form. Temperature data sources and thermal suitability analysis To generate hourly average temperature curves for the different months and evaluate the likelihood of temperatures falling within the preferred range (availability of that thermal niche), we downloaded freely available hourly temperature layers at 2 m height for the years 2009–2023 from https://power.larc.nasa.gov/ . We selected representative locations for each combination of land use and latitudinal band, resulting in four points corresponding to forested and productive sectors of both Northern and Southern Yungas. The time stamps were adjusted to local time using a UTC-3 transformation. A heatmap of daily hour versus month was generated for both average temperature and thermal-niche suitability. Thermal-niche suitability is defined as the probability of observing a temperature value within the optimal interval. We then analyzed the correspondence between the heatmap of thermal-niche suitability and the respective heatmap based on mammal activity patterns. To validate the accuracy of the temperature sensor integrated into a camera trap (Bushnell Core DS-4K No Glow), a comparison experiment was conducted using a temperature data logger (Onset HOBO MX2201, ± 0.5°C accuracy) as a reference. The data analysis revealed the presence of a non-linear bias in the camera's measurements: an underestimation at temperatures below 25°C and an overestimation at temperatures above this threshold. This opposing error pattern suggests the existence of a compensation effect within the device's measurement system. Results The database includes 33 species of medium and large terrestrial mammals (including Galea leucoblephara and Lutreolina massoia ) belonging to nine orders and 16 families. Fourteen species are classified under national threat categories (SAyDSN and SAREM, 2019). Of the total species, 31 were recorded in Jujuy and 17 in Salta province (Northern), and 17 in Tucumán province (Southern)(Table 1 ). The interaction between species-specific thermal preference and record dominance was visualized through a dual-axis RGB heatmap (Fig. 2 ). By integrating row-max normalization (representing the relative thermal preference of each species) and column-max normalization (representing the dominant species within each temperature interval), we identified a well-defined ecological core. This core niche is concentrated between 11°C and 21°C (based on interval midpoints). Within this thermal range, the 10 most dominant species of the overall assemblage reached their peak relative incidence, suggesting that these temperatures represent the optimal environmental window for the majority of the recorded mammalian community. Outside this core, the transition to pure red or green hues indicates either specialized species reaching their thermal optimum in marginal intervals or dominant species persisting in overall sub-optimal thermal conditions. The cumulative relative frequency shows that 10 species account for at least 80% of records within each system. In northern forests, Agouti ( Dasyprocta variegata) and Tapeti ( Sylvilagus brasiliensis ) were most frequent, while Tayra ( Eira barbara ) and White-eared opossum ( Didelphis albiventris ) dominated southern forests. In productive areas (north and south), both fox species were predominant (Table 2 ). The full dataset of camera-trap records was categorized into 3-hour intervals. To enhance the visualization of species replacement, the horizontal axis (time intervals) was restructured using a variance-minimization sequence ( Fig. 3 ) . This peculiar arrangement—starting from the late afternoon (15:00–18:00) and progressing toward nocturnal extremes—effectively linearized the 24-hour cycle, revealing a clear diagonal block pattern of species activity. By calculating the mean activity index for each species within this optimized sequence and ordering them accordingly along the y-axis in ascending order, a structured succession emerged (Fig. 3 , central heatmap). The resulting seriation transitions smoothly from strictly diurnal species at the beginning of the gradient to nocturnal ones at the margins, with cathemeral and crepuscular species positioned as intermediate transitions. Despite this marked individual segregation, the total community activity (Fig. 3 , top barplot) exhibited an equitable distribution across all intervals, indicating constant habitat use throughout the day-night cycle. Noticeably, the ten most dominant species were not clustered within a specific temporal guild but were instead dispersed across the entire diagonal gradient (Fig. 3 , right barplot). This distribution suggests that high-abundance species actively partition the 24-hour window to minimize temporal overlap. The contour diagram revealed a non-linear structure in the association between environmental temperature and detection temperature (Fig. 4 ). Within the intermediate temperature range—corresponding to the previously identified optimal thermal interval—the highest density of records was concentrated around the identity line (y = x). This indicates that, under thermally favorable conditions, the average temperature at detection closely matches the average environmental temperature. Such a pattern is consistent with thermoneutral behavior, where activity does not require compensatory thermal adjustment. Outside this optimal range (between 11°C and 21°C), however, a systematic deviation from the identity line was observed, forming an inverted “Z” or trapezoidal pattern that traverses the region of highest density. Under cold environmental conditions (low ambient temperatures), detection events tended to occur at temperatures higher than the environmental average. Conversely, under warm environmental conditions (high ambient temperatures), detections were concentrated at temperatures lower than the environmental average. The heatmap shows a shift in suitability of the thermal range of the season, with winter suitability thermal range generally concentrated during daytime hours, while spring and/or summer the range is concentrated during nighttime hours. We observe that in winter, the range curve shifts upward, seeking higher average temperature values, and downward in spring-summer, seeking lower temperature values (Fig. 5 a, b). According to the probability density curves , we observe that during the warmest months in the forest, the curves are bimodal, with activity concentrated during nighttime hours, while in the colder months, they are unimodal and centered around daytime hours. On the other hand, the productive sector exhibits a bimodal pattern throughout the year, with increased activity at dusk in winter and at dawn in summer (Fig. 6 ). Standardized residuals and radar charts revealed key 3-hour intervals where species shifted their activity seasonally. For example, Agouti showed increased activity during [9,12) in Dry/Temperate months and a shift toward [0,3) and [3,6) during Rainy/Warm months, reflecting behavioral adjustment to thermal conditions. Similarly, Crab-eating Fox exhibited higher activity at dawn in warm months compared to evening peaks in cooler months. These displaced radar chart polygons visually confirmed behavioral adjustments to thermal conditions across the 24-hour cycle ( Fig. 7 ) . The ten focal species displayed distinct daily activity patterns, which varied between seasons. Chi-squared tests revealed significant seasonal shifts for the majority of species, including Crab-eating Fox (χ² = 34.33, df = 7, p < 0.001), Pampas Fox (χ² = 28.79, df = 7, p < 0.001), Agouti (χ² = 137.12, df = 7, p < 0.001), White-eared opossum (χ² = 47.13, df = 7, p < 0.001), Tayra (χ² = 28.17, df = 7, p < 0.001), Tapeti (χ² = 69.99, df = 7, p < 1.48e-12) and Southern Tapir (χ² = 55.69, df = 7, p < 0.001). Other species, such as Ocelot, Gray Brocket and Collared Peccary, showed no significant differences between seasons ( Fig. 7 ) . Table 1 Frequency of records of the species in forest and productive stations from three provinces, Salta, Jujuy and Tucumán in the Yungas of Argentina. Conservation status (Cons St) according to SGAyDSN and SAREM, 2019. LC: Least Concern, NT: Near Threatened, VU: Vulnerable, DD: Insufficient Data and CR: Critically Endangered. Species Common name Cons St Northern Southern Jujuy Salta Tucumán Total Sylvilagus brasiliensis Tapeti LC 736 179 328 1243 Subulo gouazoubira Gray Brocket LC - 7 - 7 Mazama americana Red Brocket VU - 7 - 7 Pecari tajacu Collared Peccary VU 248 22 137 407 Didelphis albiventris White-eared Opossum LC 183 19 575 777 Lutreolina massoia Massoia´s Lutrine Opossum NT 50 - 140 190 Tapirus terrestris Lowland Tapir VU 380 6 - 386 Leopardus wiedii Margay VU 61 5 - 66 Puma concolor Puma LC 32 2 2 36 Leopardus geoffroyi Geoffroy's Cat LC 67 - 29 96 Panthera onca Jaguar CR 6 - - 6 Leopardus pardinoides Tiger Cat VU 4 - - 4 Leopardus colocolo Pampas Cat VU 2 - - 2 Leopardus pardalis Ocelot VU 120 11 382 513 Herpailurus yagouaroundi Jaguarundi LC 38 7 6 51 Lontra longicaudis Neotropical Otter NT - - 23 23 Galictis cuja Lesser Grison LC 10 - 2 12 Eira barbara Tayra NT 514 140 971 1625 Nasua nasua South American Coati LC 108 14 - 122 Procyon cancrivorus Crab-eating Raccoon LC 41 - 207 248 Conepatus chinga Common Hog-nosed Skunk LC 30 2 32 Lycalopex gymnocercus Pampa Fox LC 513 - 445 958 Cerdocyon thous Crab-eating Fox LC 736 179 328 1243 Euphractus sexcinctus Yellow Armadillo LC 127 55 15 197 Chaetophractus villosus Large Hairy Armadillo LC 1 - - 1 Dasypus novemcinctus Nine-banded Armadillo LC 4 - - 4 Dasypus mazzai Yepes’s Mulita DD 2 - - 2 Hydrochoerus hydrochaeris Capybara LC 1 - - 1 Galea leucoblephara Common Cavy LC 2 - - 2 Dasyprocta variegata Agouti LC 2267 45 - 2312 Myrmecophaga tridactyla Giant Anteater VU 1 - - 1 Tamandua tetradactyla Collared Anteater NT 15 1 2 18 Sapajus cay Azara’s Capuchin VU 9 - - 9 Table 2 Relative and cumulative frequencies expressed in %, of mammal records across four geographic systems: Northern and Southern, Forest and Productive area, as well as the combined total. Relative frequency (RF) refers to the percentage of records represented by each species within the total records for each system, as well as for the entire study region. Cumulative frequency (CF) indicates the progressive sum of relative frequencies, with species ordered from most to least abundant in the pooled dataset, showing their cumulative contribution. In bold highlight the species included in the most explanatory subset for each system. Northern (Salta-Jujuy) Southern (Tucumán) Forest Productive Forest Productive Total Species RF CF RF CF RF CF RF CF RF CF Dasyprocta variegata 34,48 34,48 0,11 0,11 0,00 0,00 0,00 0,00 20,63 20,63 Eira barbara 9,62 44,11 1,00 1,11 30,64 30,64 1,52 1,52 14,50 35,12 Cerdocyon thous 9,77 53,88 28,86 29,97 8,58 39,22 12,61 14,13 11,09 46,21 Sylvilagus brasiliensis 14,70 68,58 3,55 33,52 3,18 42,40 1,09 15,22 10,01 56,22 Lycalopex gymnocercus 0,82 69,40 50,83 84,35 3,05 45,45 75,87 91,09 8,55 64,77 Didelphis albiventris 3,01 72,41 0,00 84,35 17,93 63,38 2,39 93,48 6,93 71,70 Subulo gouazoubira 7,28 79,69 1,22 85,57 7,02 70,41 3,48 96,96 6,57 78,27 Leopardus pardalis 1,94 81,63 0,11 85,68 12,02 82,42 0,87 97,83 4,58 82,84 Pecari tajacu 3,98 85,62 0,33 86,02 4,35 86,78 0,00 97,83 3,63 86,48 Tapirus terrestris 5,76 91,38 0,00 86,02 0,00 86,78 0,00 97,83 3,44 89,92 Total count of records 6702 901 3146 460 11209 Total percentage 59,79 8,04 28,07 4,10 100,00 Discussion We observed that mammals in the Yungas occupy or seek their thermal niche, generally ranging between 11 and 21°C (thermal activity window) across seasons. In forested areas, activity curves were typically bimodal during the warmest months, with peaks concentrated at night, whereas in colder months, activity tended to be unimodal and centered around daytime hours. In contrast, in the productive matrix, activity followed a bimodal pattern year-round, with increased activity at dusk during winter and at dawn during summer. These patterns suggest that mammals flexibly adjust their temporal activity to track suitable thermal conditions, modulated by both season and landscape context. The observed structure in the activity–environmental thermal space indicates compensatory behavioral thermal adjustment at the extremes of the purported optimal thermal interval, whereby activity shifts toward conditions closer to the optimal range. In contrast, within the thermoneutral interval, activity closely tracks ambient temperatures without detectable bias. During warmer months, many species increase nocturnal activity, whereas in cooler periods activity is more frequently concentrated during daytime or crepuscular hours. Together, these patterns support the hypothesis that species modulate their temporal activity to maintain thermal conditions near their preferred niche. Most mammals recorded in our study were primarily nocturnal, supporting the idea that nocturnality is an ancestral trait in the group (Heesy and Hall, 2010 ), however various pressures are pushing mammals towards nocturnality. Increasing scientific research suggests that human disturbances—ranging from urban development and agriculture to lethal and nonlethal activities—are driving a global increase in nocturnal activity across numerous mammal species (Gaynor et al. 2018 ). Although we did not find sufficient evidence to classify any species as strictly crepuscular, predictive expectations suggest that such species, if present, may alternate between morning and evening peaks depending on the season. Among the photographed mammals, ten focal species accounted for the majority of detection events across both seasonal periods, highlighting their ecological relevance and suitability as indicators of temporal activity shifts. Within the nocturnal species, opossums present well-developed eyes, displaying features that reflect specialization to scotopic vision such as the tapetum lucidum, a membrane positioned in the back of the eye capable of reflecting the light, thus accentuating the contrast of objects, and improving vision in low light conditions (Hokoç et al. 2012 ). In this work, White-eared opossum displayed a predominantly nocturnal pattern. In winter, however, it was frequently detected during daytime hours, particularly at dusk and early evening, indicating that it is not strictly nocturnal. Conversely, during summer, activity peaked in the early morning rather than at evening twilight, possibly associated with the lowest temperature range during the 24 hours, Tripodi et al. ( 2023 ) found D. aurita associated with the lowest temperatures. This seasonal pattern is clearly visible in separate radar charts for warm and cool periods, illustrating temporal shifts in activity intensity. The Crab-eating Fox, is an omnivorous mesocarnivore with nocturnal and crepuscular habits (Faria-Corrêa et al. 2009 ; Santos et al. 2024 ). In sites exposed to anthropic pressures, this species showed less nocturnal activity and a higher peak of activity in the early morning (Santos et al. 2024 ). In forested areas, activity was bimodal during the warm/rainy season and shifted toward crepuscular and early daytime hours in the dry/temperate season. In productive landscapes, nocturnal activity was reduced, and early morning activity became more prominent, while the overall temporal pattern remained consistent across natural vegetation types. These findings suggest that Crab-eating Fox flexibly adjusts its temporal activity to track its thermal niche and potentially to coexist with competitors or exploit anthropogenic resources, without necessarily becoming more strictly nocturnal in disturbed habitats, consistent with observations from other Neotropical regions (Faria-Corrêa et al. 2009 ; Albanesi et al. 2016 ; Santos et al. 2024 ). Lowland tapir activity shifted toward nocturnality during warm months, concentrating at night and dawn while remaining virtually absent during the day. Dry season activity was bimodal, peaking at dusk and early night. These results support the thermal stress hypothesis, consistent with other Neotropical studies where tapirs avoid heat through nocturnal shifts (Foerster & Vaughan 2002 ; Medici 2010 ; Ayala 2003 ; Burs et al. 2023 ). Such patterns might also reflect seasonal resource shifts or lower nocturnal human disturbance. The Tapeti is a generalist mammal, commonly living in transition environments among forests, open fields and the edge of watercourses (Reis et al., 2011 , Albanesi et al., 2026 ) and like the tapir, tends to be more active at dusk and in the early evening during the winter, when more heat is retained in the ground. It exhibits a major shift in activity towards the night hours in the rainy season. It is possible that in winter many species extend their foraging range, probably associated with a lower food availability, requiring a longer time frame to feed. Among cathemeral species, Cox and Gaston ( 2024 ) state that cathemeral richness is highest in regions with high environmental seasonality and in mountainous regions, and additionally, their activity may increase following habitat fragmentation. In this work we find the Gray Brocket deer using all hours of the day and night in summer, but making greater use of the afternoon and evening/dusk in winter; however, this pattern was not as pronounced and did not show significant differences. Morello et al. ( 2018 ) observed a behavior that avoids the exposition to the highest temperatures during the snap time, which can reach up to 45°C in the summer time (Morello et al. 2018 ). However, this species is considered the most ecologically flexible neotropical deer, surviving in anthropogenic landscapes (Ferreguetti et al. 2015 ). For instance, the species has been recorded in human-modified landscapes, including Eucalyptus plantations, sugarcane crops, and isolated forest remnants immersed into agricultural matrix (Black-Décima et al. 2010; Magioli et al. 2014 , 2016 ). Records of activity patterns were consistent with literature, which mentions changes in the feeding behaviour of Gray Brocket deer according to seasonality. During the spring and summer seasons, is mainly found during the twilight periods, with individuals primarily active during dawn and dusk (Rivero et al. 2004 ; Pautasso et al. 2008; Barrientos and Maffei 2000 ; Noss et al. 2003 ; Ferreguetti et al. 2015 ; de Oliveira et al. 2016 ; Grotta Neto et al. 2019). During autumn and winter, it would use all daily hours for foraging, besides dawn and dusk, something reported previously in other small ruminants (Putman 1988 ). This behavioral strategy, which prioritizes quality over quantity, would be responsible for the rhythm of activity previously described, which invests a lot of time in food selection (Richard and Juliá 2004 ). The Pampas fox inhabits primarily open environments and exhibits significant ecological and behavioral plasticity, even in human-disturbed landscapes (Lucherini and Luengos Vidal 2008 ; Gorosábel et al. 2024 ; Vidal et al. 2025 ). Our findings show a strong association between this species and the production system, with activity shifting toward morning twilight in summer and evening in winter. While the fox exploits abundant rodent prey within these sugarcane fields, nocturnal prey behavior and harvesting schedules likely restrict its use of the area to nighttime and specific seasons (Busch et al. 2000 ). Diurnal species, such as the tayra and agouti, supported our hypothesis by shifting activity toward twilight hours to mitigate thermal stress while remaining within 'illuminated activity' periods. Beyond these limits, activity is presumably reduced due to high physiological or adaptive costs (Bennie et al. 2014 ). For instance, Cid et al. (2015) observed D. azarae in the Brazilian Pantanal shifting from a single activity core on cold days to a bimodal pattern on hot days, compensating for midday reductions with afternoon bouts to maintain a constant activity range. This positive correlation between temperature and Dasyprocta spp. activity aligns with several studies (Gómez et al. 2005 ; Suselbeek et al. 2014 ; García-Restrepo et al. 2019; Mendoza et al. 2019 ). Furthermore, while food quality remains a critical driver (Bourgoin et al. 2008 ), the tayra’s reliance on areas near water, which offer cooler microclimates, likely offsets the metabolic demands of its diurnal lifestyle (Bianchi et al. 2020 ; McNab 1995 ). In conclusion, these seasonal shifts represent a behavioral modulation mechanism for tracking a specific thermal niche, where species dynamically reconfigure their daily activity to remain within a favorable 11–21°C window. Overall, our findings suggest that time acts as a manageable resource to mitigate thermal stress and minimize physiological costs. The strong correlation between temperature and activity patterns across the assemblage indicates that thermal availability is a primary structuring force in the Southern Andean Yungas. Our findings suggest that as global temperatures rise, the availability of thermal refugia, such as dense forest cover and riparian corridors, will become critical for mammalian persistence. Without these buffered microclimates, species may be forced into an increased reliance on nocturnality to escape heat stress. This temporal compression not only limits diurnal foraging opportunities but also increases the risk of new competitive interactions. Declarations Research funding This research did not receive funding. Author Contribution S.A. and D.S. wrote the main manuscript text , prepared figures . All authors reviewed the manuscript. Acknowledgments This work was conducted within the framework of a collaborative agreement between the Instituto de Biodiversidad Neotropical (IBN, Conicet-UNT) and the Fundación ProYungas. The primary objective of this agreement is to generate scientific information to strengthen decision-making processes in both public and private spheres. We are grateful to the Fundación ProYungas for granting us access to their camera trap database, and to Luciana Cristobal for producing the map. Finally, this research would not have been possible without the support of the National Scientific and Technical Research Council (CONICET) and the Universidad Nacional del Tucumán (UNT). References Albanesi SA, Cáceres R, Novillo A, Bellis LM (2026) The importance of riparian forests as corridors for mammals in a productive landscape of the Yungas. J Nat Conserv 91:127213 Albanesi SA, Jayat JP, Alejandro B (2016) Patrones de actividad de mamíferos de medio y gran porte en el pedemonte de Yungas del noroeste argentino. Mastozoología neotropical 23:335–358 Ayala GMC (2003) Monitoreo de Tapirus terrestris en el Izozog (Cerro Cortado) Mediante el Uso de Telemetría como Base para un Plan de Conservación. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9369097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622522897,"identity":"7d1ef720-b180-4f49-955e-44513022a275","order_by":0,"name":"Sebastián Alejandro Albanesi","email":"data:image/png;base64,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","orcid":"","institution":"Instituto de Biodiversidad Neotropical (IBN, CONICET-UNT), Cúpulas de Horco Molle. Yerba Buena","correspondingAuthor":true,"prefix":"","firstName":"Sebastián","middleName":"Alejandro","lastName":"Albanesi","suffix":""},{"id":622522898,"identity":"d327f31e-fce6-4337-83d8-3ac458a8bdf0","order_by":1,"name":"Daniel Andrés Dos Santos","email":"","orcid":"","institution":"Instituto de Biodiversidad Neotropical (IBN, CONICET-UNT), Cúpulas de Horco Molle. Yerba Buena","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Andrés Dos","lastName":"Santos","suffix":""}],"badges":[],"createdAt":"2026-04-09 13:13:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9369097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9369097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107314277,"identity":"21909eeb-659b-42c2-94a2-5f02610e434b","added_by":"auto","created_at":"2026-04-20 09:36:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":947215,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of camera traps across forest (red circles) and productive (blue circles) land-use areas within the Argentine Yungas. Insets provide a closer view of the two areas with the highest sampling density.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/dd0aa153077abf6f6003b04f.jpg"},{"id":107705453,"identity":"70414e8a-573d-4a25-891f-cfe4d0b959b5","added_by":"auto","created_at":"2026-04-24 09:12:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191202,"visible":true,"origin":"","legend":"\u003cp\u003eRelative incidence of mammal species across temperature intervals. Top marginal barplot: Total observation counts per temperature interval (log-transformed). Right marginal barplot: Total observation counts per species (log-transformed). Bar intensity (grayscale) represents the proportional contribution of each interval to the total records. Central heatmap: Visualization of species occurrence using a two-dimensional RGB color space. Green (G-channel) represents the incidence of a species relative to its own maximum across all temperature intervals (row-max normalization). It highlights the “preferred\" temperature range for each species. Red (R-channel) represents the incidence of a species relative to the maximum observed within a specific temperature interval (column-max normalization). It highlights which species dominate each temperature category. Yellow colour indicates a convergence where a species is both at its own activity peak and is the dominant species for that temperature.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/8cfab85d8f0ca4913551aa58.jpeg"},{"id":107314279,"identity":"27cbcf86-c456-4054-94cc-40e835fd2cbe","added_by":"auto","created_at":"2026-04-20 09:36:32","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136738,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal activity patterns of mammal species in Argentina's Yungas forests. Top barplot: Total observation counts per 3-hour interval (log-transf.). Right barplot: Total observation counts per species (log-transf.). In all barplots, shading (grayscale) is proportional to the raw counts relative to the global maximum, highlighting the most conspicuous species and time intervals. Central heatmap: Species-specific temporal occurrence. Cell intensity (grayscale) represents the incidence of each species relative to its own maximum peak (row-max normalization). The X-axis follows a variance-minimization sequence—starting from late afternoon (15:00–18:00) and progressing toward nocturnal extremes—revealing a clear diagonal seriation of the assemblage.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/12d77ac8cd202a45224c9021.jpeg"},{"id":107486207,"identity":"ab91d9d5-d345-47bf-a87b-9ceadd3e9d8a","added_by":"auto","created_at":"2026-04-22 02:37:47","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196969,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-dimensional kernel density estimate (KDE) of the relationship between average environmental temperature and temperature at detection events in the Southern Andean Yungas. Filled contours represent cumulative probability levels. The white inverted Z-shaped line crosses the area of highest density, with its central segment aligning with the identity line within the thermoneutral interval. Deviations at low and high temperatures indicate compensatory behavioral adjustments, with activity occurring under relatively warmer conditions when cold and cooler conditions when warm.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/263f91ba463574b435677d2c.jpeg"},{"id":107314280,"identity":"2cd04bff-92b3-4b3e-8449-7f3fc90aece4","added_by":"auto","created_at":"2026-04-20 09:36:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116174,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in environmental temperature and thermal suitability in the Southern Andean Yungas. (a) Mean environmental temperature (°C) by month and hour of day. (b) Thermal suitability, expressed as the proportion of records within the putative optimal range (11–21°C), by month and hour. Shaded areas indicate nighttime (from dusk to dawn), and dotted lines represent sunrise and sunset times across the year.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/7d6909033939ee07425acc4c.jpg"},{"id":107314281,"identity":"b62aa719-317b-44bc-ad9c-b66c6d4fb94e","added_by":"auto","created_at":"2026-04-20 09:36:32","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153619,"visible":true,"origin":"","legend":"\u003cp\u003eHourly density distributions by season in the Northern and Southern Yungas regions.\u003c/p\u003e","description":"","filename":"Figure6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/3e1d2443a48445d6f9899e40.jpeg"},{"id":107486176,"identity":"030681f7-b033-48be-83b9-efd1dfc315f6","added_by":"auto","created_at":"2026-04-22 02:37:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":653781,"visible":true,"origin":"","legend":"\u003cp\u003eCircular radar plot showing proportional activity across 3-hour intervals for the Dry/Temperate (blue) and Rainy/Warm (red) seasons. Daily activity patterns of the 10 most common species in the Southern Andean Yungas are represented. Time intervals are arranged counterclockwise starting at [0–3). Differences in polygon shape and extent highlight shifts in relative activity between seasons. For each season, a synthetic resultant vector summarizes the circular distribution of activity: its direction indicates the predominant timing of activity concentration, and its length reflects the degree of temporal aggregation (i.e., how strongly activity is concentrated around a specific period of the day). Animal illustrations were reproduced from de Angelo et al. (2017).\u003c/p\u003e","description":"","filename":"Figura7.png","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/b0295c190a8e144377b2ad44.png"},{"id":109249339,"identity":"0c527607-33d3-45ee-aeaf-87df991aff82","added_by":"auto","created_at":"2026-05-14 08:48:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2722689,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9369097/v1/41e21a80-d956-4088-81ef-d94050ba3633.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Thermal Availability to Mammal Activity Patterns through Long-Term Camera Trap Monitoring in the Southern Andean Yungas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMammals are endothermic animals, capable of generating their own body heat to maintain vital functions without directly depending on environmental conditions. However, temperature, natural light cycles and human activities play a fundamental role in their physiology and behavior. Many mammals exhibit daily activity rhythms influenced by changes in ambient temperature, suggesting that this factor is key to patterns of speciation and diversification in mammals among other vertebrates.\u003c/p\u003e \u003cp\u003eIn the face of climate change and habitat alteration, understanding species\u0026rsquo; thermal niche breadth is critical for developing conservation strategies and identifying priority areas for protection. While organisms can respond to environmental shifts through phenotypic plasticity, dispersal, or genetic adaptation, the latter two are often constrained under rapid change. Dispersal may be limited by habitat fragmentation, and genetic adaptation can be too slow for long-lived species. Phenotypic plasticity\u0026mdash;encompassing morphological, physiological, and behavioral changes\u0026mdash;thus becomes a key response (Piersma \u0026amp; Drent, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Behavioral adjustments, in particular, can occur quickly and reversibly, making them especially relevant under abrupt environmental shifts such as the recent rise in global temperatures (Van Buskirk, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn warm environments, nighttime can serve as a thermal refuge for many mammals, helping them avoid the extreme heat of the day. Shifting activity to cooler periods\u0026mdash;especially at night\u0026mdash;is a widespread behavioral strategy to cope with rising temperatures (Berry et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hetem et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McFarland et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Indeed, most mammals are nocturnal, accounting for 69% of species in the global dataset analyzed by Bennie et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), compared to 20% diurnal, 8.5% cathemeral, and 2.5% crepuscular. Nocturnality not only mitigates thermal stress but also reduces the likelihood of encounters with humans, acting as either a facultative or obligate response.\u003c/p\u003e \u003cp\u003eBennie et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) show that diel activity patterns\u0026mdash;functional traits shaped by physiological and morphological adaptations\u0026mdash;are strongly influenced by climate and the availability of biologically useful daylight. Diurnal activity tends to be more common in regions with cold nights, where the energetic cost of nocturnality is high, while nocturnal species dominate in arid and warm regions. In the Neotropics, including the Yungas, nocturnal activity patterns are also prevalent among mammals (Albanesi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), underscoring the dual adaptive value of nighttime activity in response to both environmental conditions and anthropogenic pressures. These temporal strategies reflect the interplay between thermal constraints, light environments, and increasing human disturbance, all of which shape species\u0026rsquo; current and potentially future distributions.\u003c/p\u003e \u003cp\u003eThe use of camera traps in wildlife research has surged since the early 2000s (Burton et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), offering a non-invasive and efficient tool to monitor elusive species and remote areas (Rovero \u0026amp; Zimmermann, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These devices are now widely applied in terrestrial vertebrate studies for purposes ranging from species inventories and population density estimates to behavior and movement analyses (Tobler et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gilbert et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Niedballa et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent advances in camera trap technology have enabled rapid and systematic monitoring of terrestrial vertebrate communities, particularly medium and large bodied species, and have opened new avenues for regional and global scale ecological research (Steenweg et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although most studies remain site specific, a growing number are pooling data across networks to address broader conservation and biogeographic questions (Beaudrot et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Davis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rovero et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), emphasizing the potential of camera trap datasets to inform global assessments. In our study, we take advantage of a 14 year continuous dataset, professionally vetted for species identification, to explore how mammalian activity patterns are structured in relation to temperature as well as ecological features of landscapes. By incorporating an underused variable\u0026mdash;ambient temperature recorded at each detection event\u0026mdash;we aim to delineate the thermal activity niche of the mammalian assemblage in a productive landscape of the Yungas ecoregion.\u003c/p\u003e \u003cp\u003eBeyond documenting species presence and activity, our approach leverages the positive detection data\u0026mdash;that is, instances where individuals were actually recorded\u0026mdash;to estimate the thermal preferences of each species. We treat these temperature-associated records as behavioral evidence of realized thermal niches, assuming that animals are more likely to be active under favorable thermal conditions. Based on this, we delineate species-specific thermal preference ranges from observed data. We then assess how the availability of these preferred thermal conditions varies across the daily cycle throughout different months of the year, effectively quantifying the temporal offer of thermally optimal windows. This framework allows us to examine whether and how species adjust their activity patterns seasonally in response to the shifting availability of their thermal optima. Crucially, this analysis includes species with different diel habits\u0026mdash;diurnal, nocturnal, cathemeral, crepuscular\u0026mdash;revealing instances where individuals diverge from their usual temporal niches to take advantage of more favorable thermal windows. Such flexibility, or lack thereof, may have significant implications for species\u0026rsquo; resilience under warming conditions.\u003c/p\u003e \u003cp\u003eTo understand how thermal conditions shape mammal activity patterns in subtropical montane forests, we used a 14-year camera trap dataset to estimate species-specific thermal preferences based on positive detection events. We then assessed the daily and seasonal availability of these preferred temperature ranges and evaluated how closely species' activity aligns with this thermal niche across regions (Northern vs. Southern Yungas), months, and landscape contexts (Forest vs. Productive). We hypothesize that cathemeral species will exhibit a crepuscular pattern aligning with thermally favorable dawn and dusk periods, modulated by season; specifically, they will be more active at night during summer and during the day in winter. Nocturnal species are expected to concentrate activity during summer nights, while in winter, they may reduce nocturnality, shifting activity toward twilight or even daytime hours to track their thermal optimum. Conversely, diurnal species will shift activity toward crepuscular hours during hot months to avoid thermal stress. Ultimately, this framework will help identify robust indicator species for monitoring mammal responses to environmental change.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe Southern Andean Yungas ecoregion is a montane forest system that stretches across the departments of Santa Cruz, Chuquisaca, and Tarija in southern Bolivia, and through the provinces of Salta, Jujuy, Tucum\u0026aacute;n and Catamarca in northwestern Argentina, spanning a latitudinal gradient between 22\u0026deg; and 29\u0026deg;S. The Yungas forest system is distributed along mountain slopes over an altitudinal gradient ranging from 400 to 2,500 meters above sea level (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Most camera trap records are concentrated in the piedmont zone below 700 meters above sea level. Along this elevation gradient, vegetation is organized into altitudinal belts, each with distinct physiognomic characteristics (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The Southern Yungas host the greatest biodiversity in Argentina, granting them high conservation value. Additionally, they play a critical hydrological role, as most rivers that supply key urban centers and agricultural areas in the region originate in these forests (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrecipitation in the Southern Yungas follows a seasonal pattern, with 75\u0026ndash;80% of rainfall occurring during the summer months, from November to March (Minetti, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). During the dry season, from May to August, frequent mist events partially compensate for the lack of rainfall, which is why these forests are often referred to as \u0026ldquo;cloud forests\u0026rdquo; (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The annual mean temperature ranges from 20.2\u0026deg;C in the northern portion of the ecoregion to 14.8\u0026deg;C in the southern sectors, with transitional zones near the Chaco region reaching up to 23.8\u0026deg;C. Some sites in the Yungas experience an annual thermal amplitude of up to 8\u0026deg;C, with August showing the greatest variation. Spring temperatures tend to be higher than those in autumn. Absolute minimum temperatures can reach \u0026minus;\u0026thinsp;3\u0026deg;C to \u0026minus;\u0026thinsp;4\u0026deg;C, with the first frosts typically occurring in June or July, and no frosts recorded after mid-September. Temperature is strongly and inversely correlated with elevation, and additionally, slope aspect has a local effect on both solar radiation and site humidity. The Yungas, like the Paran\u0026aacute; Forest, are characterized by high levels of forest exploitation, hunting, subsistence livestock farming, and commercial ranching. These activities vary along the marked altitudinal and latitudinal gradients present in the region (Nanni et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCamera trapping and data acquisition\u003c/h3\u003e\n\u003cp\u003eWe worked with a database comprising 44 independent surveys conducted between 2009 and 2023 in various habitats, including continuous and riparian forests, as well as plantations of sugarcane, lemon, and forestry, depending on the objective of each survey. A total of 573 camera traps were deployed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a cumulative effort of 20,871.45 camera trap days. The surveys took place across three provinces within the Yungas ecoregion of Argentina. Sampling coverage showed a decreasing gradient from Jujuy (344 cameras, 12,312.34 camera trap days) to Salta (44 cameras, 1,806.42 camera trap days), with Tucum\u0026aacute;n occupying an intermediate position (185 cameras, 6,752.69 camera trap days).\u003c/p\u003e\n\u003ch3\u003eSampling designs\u003c/h3\u003e\n\u003cp\u003eSpecies were recorded using camera traps of various brands and models (Moultrie DGS-M40, Bushnell Trophy Cam HD, Bushnell Trophy Cam HD Aggressor, Bushnell Prime, Bushnell Prime No Glow, Bushnell Core DS-4K). Each sampling station consisted of a single camera aimed at bait (a can of tuna) placed approximately 3 meters away, located off trails and paths, at a height of 50 cm above the ground. Cameras were programmed to take three consecutive photographs with a 5 minute interval between photo groups. The cameras also recorded temperature data. For this study, we used all species records but considered only one photo per group of three.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSpecies selection and temporal activity analysis\u003c/h3\u003e\n\u003cp\u003eOur study centered on ten mammal species, which together comprised the vast majority of detection events and encompassed a variety of body sizes and trophic guilds. For each independent detection event, the time of capture was extracted and assigned to one of eight 3-hour intervals: [0,3), [3,6), [6,9), [9,12), [12,15), [15,18), [18,21) and [21,24). Each record was also classified according to the season: Dry/Temperate (April\u0026ndash;September) and Rainy/Warm (October\u0026ndash;March). For each species, we built contingency tables summarizing the frequency of detection events across hourly intervals and seasons. A chi-squared test of homogeneity (χ\u0026sup2;) was applied to assess whether daily activity distributions differed significantly between seasons. Standardized residuals were inspected to identify specific time intervals contributing most to seasonal variation. Daily activity patterns were visualized using radar charts (8 sectors corresponding to the 3-hour intervals) that display the relative frequency of detections for each season. The resulting paired polygons (Dry/Temperate vs. Rainy/Warm) provide a direct graphical representation of potential shifts in activity intensity and timing. Radar charts were produced in R 4.3.3 (R Core Team, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using the \u003cem\u003efmsb\u003c/em\u003e package. Significant seasonal differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were interpreted as evidence of behavioral adjustments to thermal conditions, particularly when species shifted activity from diurnal to nocturnal or crepuscular periods during warmer months.\u003c/p\u003e \u003cp\u003eThe relationship between average environmental temperature and temperature at detection events was examined using a two-dimensional kernel density estimation (2D kde), from which cumulative probability contours were derived to identify areas of highest observation density. To summarize the main structure of this relationship, we overlaid an inverted Z-shaped piecewise linear model on the density surface. The lower and upper bounds of the trapezoid were identified through an exhaustive search procedure, selecting the combination of breakpoints that minimized the mean squared error relative to the observed values. This simple descriptive model captures the alignment with the identity line within the thermoneutral range and the tendency for detection temperatures to converge toward its lower and upper bounds at the extremes of the environmental gradient. The trapezoidal fit is intended as a parsimonious approximation of the dominant pattern, which may in reality follow a smoother nonlinear form.\u003c/p\u003e\n\u003ch3\u003eTemperature data sources and thermal suitability analysis\u003c/h3\u003e\n\u003cp\u003eTo generate hourly average temperature curves for the different months and evaluate the likelihood of temperatures falling within the preferred range (availability of that thermal niche), we downloaded freely available hourly temperature layers at 2 m height for the years 2009\u0026ndash;2023 from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://power.larc.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://power.larc.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. We selected representative locations for each combination of land use and latitudinal band, resulting in four points corresponding to forested and productive sectors of both Northern and Southern Yungas. The time stamps were adjusted to local time using a UTC-3 transformation. A heatmap of daily hour versus month was generated for both average temperature and thermal-niche suitability. Thermal-niche suitability is defined as the probability of observing a temperature value within the optimal interval. We then analyzed the correspondence between the heatmap of thermal-niche suitability and the respective heatmap based on mammal activity patterns. To validate the accuracy of the temperature sensor integrated into a camera trap (Bushnell Core DS-4K No Glow), a comparison experiment was conducted using a temperature data logger (Onset HOBO MX2201, \u0026plusmn;\u0026thinsp;0.5\u0026deg;C accuracy) as a reference. The data analysis revealed the presence of a non-linear bias in the camera's measurements: an underestimation at temperatures below 25\u0026deg;C and an overestimation at temperatures above this threshold. This opposing error pattern suggests the existence of a compensation effect within the device's measurement system.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe database includes 33 species of medium and large terrestrial mammals (including \u003cem\u003eGalea leucoblephara\u003c/em\u003e and \u003cem\u003eLutreolina massoia\u003c/em\u003e) belonging to nine orders and 16 families. Fourteen species are classified under national threat categories (SAyDSN and SAREM, 2019). Of the total species, 31 were recorded in Jujuy and 17 in Salta province (Northern), and 17 in Tucum\u0026aacute;n province (Southern)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The interaction between species-specific thermal preference and record dominance was visualized through a dual-axis RGB heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By integrating row-max normalization (representing the relative thermal preference of each species) and column-max normalization (representing the dominant species within each temperature interval), we identified a well-defined ecological core. This core niche is concentrated between 11\u0026deg;C and 21\u0026deg;C (based on interval midpoints). Within this thermal range, the 10 most dominant species of the overall assemblage reached their peak relative incidence, suggesting that these temperatures represent the optimal environmental window for the majority of the recorded mammalian community. Outside this core, the transition to pure red or green hues indicates either specialized species reaching their thermal optimum in marginal intervals or dominant species persisting in overall sub-optimal thermal conditions.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003ecumulative relative frequency\u003c/b\u003e shows that 10 species account for at least 80% of records within each system. In northern forests, Agouti (\u003cem\u003eDasyprocta\u003c/em\u003e variegata) and Tapeti (\u003cem\u003eSylvilagus brasiliensis\u003c/em\u003e) were most frequent, while Tayra (\u003cem\u003eEira barbara\u003c/em\u003e) and White-eared opossum (\u003cem\u003eDidelphis albiventris\u003c/em\u003e) dominated southern forests. In productive areas (north and south), both fox species were predominant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe full dataset of camera-trap records was categorized into 3-hour intervals. To enhance the visualization of species replacement, the horizontal axis (time intervals) was restructured using a variance-minimization sequence \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This peculiar arrangement\u0026mdash;starting from the late afternoon (15:00\u0026ndash;18:00) and progressing toward nocturnal extremes\u0026mdash;effectively linearized the 24-hour cycle, revealing a clear diagonal block pattern of species activity. By calculating the mean activity index for each species within this optimized sequence and ordering them accordingly along the y-axis in ascending order, a structured succession emerged (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, central heatmap). The resulting seriation transitions smoothly from strictly diurnal species at the beginning of the gradient to nocturnal ones at the margins, with cathemeral and crepuscular species positioned as intermediate transitions. Despite this marked individual segregation, the total community activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, top barplot) exhibited an equitable distribution across all intervals, indicating constant habitat use throughout the day-night cycle. Noticeably, the ten most dominant species were not clustered within a specific temporal guild but were instead dispersed across the entire diagonal gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, right barplot). This distribution suggests that high-abundance species actively partition the 24-hour window to minimize temporal overlap.\u003c/p\u003e \u003cp\u003eThe contour diagram revealed a non-linear structure in the association between environmental temperature and detection temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Within the intermediate temperature range\u0026mdash;corresponding to the previously identified optimal thermal interval\u0026mdash;the highest density of records was concentrated around the identity line (y\u0026thinsp;=\u0026thinsp;x). This indicates that, under thermally favorable conditions, the average temperature at detection closely matches the average environmental temperature. Such a pattern is consistent with thermoneutral behavior, where activity does not require compensatory thermal adjustment.\u003c/p\u003e \u003cp\u003eOutside this optimal range (between 11\u0026deg;C and 21\u0026deg;C), however, a systematic deviation from the identity line was observed, forming an inverted \u0026ldquo;Z\u0026rdquo; or trapezoidal pattern that traverses the region of highest density. Under cold environmental conditions (low ambient temperatures), detection events tended to occur at temperatures higher than the environmental average. Conversely, under warm environmental conditions (high ambient temperatures), detections were concentrated at temperatures lower than the environmental average.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eheatmap\u003c/b\u003e shows a shift in suitability of the thermal range of the season, with winter suitability thermal range generally concentrated during daytime hours, while spring and/or summer the range is concentrated during nighttime hours. We observe that in winter, the range curve shifts upward, seeking higher average temperature values, and downward in spring-summer, seeking lower temperature values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b).\u003c/p\u003e \u003cp\u003eAccording to the \u003cb\u003eprobability density curves\u003c/b\u003e, we observe that during the warmest months in the forest, the curves are bimodal, with activity concentrated during nighttime hours, while in the colder months, they are unimodal and centered around daytime hours. On the other hand, the productive sector exhibits a bimodal pattern throughout the year, with increased activity at dusk in winter and at dawn in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStandardized residuals and radar charts revealed key 3-hour intervals where species shifted their activity seasonally. For example, Agouti showed increased activity during [9,12) in Dry/Temperate months and a shift toward [0,3) and [3,6) during Rainy/Warm months, reflecting behavioral adjustment to thermal conditions. Similarly, Crab-eating Fox exhibited higher activity at dawn in warm months compared to evening peaks in cooler months. These displaced radar chart polygons visually confirmed behavioral adjustments to thermal conditions across the 24-hour cycle \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The ten focal species displayed distinct daily activity patterns, which varied between seasons. Chi-squared tests revealed significant seasonal shifts for the majority of species, including Crab-eating Fox (χ\u0026sup2; = 34.33, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Pampas Fox (χ\u0026sup2; = 28.79, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Agouti (χ\u0026sup2; = 137.12, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), White-eared opossum (χ\u0026sup2; = 47.13, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Tayra (χ\u0026sup2; = 28.17, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Tapeti (χ\u0026sup2; = 69.99, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;1.48e-12) and Southern Tapir (χ\u0026sup2; = 55.69, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other species, such as Ocelot, Gray Brocket and Collared Peccary, showed no significant differences between seasons \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\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\u003eFrequency of records of the species in forest and productive stations from three provinces, Salta, Jujuy and Tucum\u0026aacute;n in the Yungas of Argentina. Conservation status (Cons St) according to SGAyDSN and SAREM, 2019. LC: Least Concern, NT: Near Threatened, VU: Vulnerable, DD: Insufficient Data and CR: Critically Endangered.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons St\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJujuy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTucum\u0026aacute;n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSylvilagus brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTapeti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubulo gouazoubira\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGray Brocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMazama americana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed Brocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePecari tajacu\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollared Peccary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDidelphis albiventris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite-eared Opossum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLutreolina massoia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMassoia\u0026acute;s Lutrine Opossum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTapirus terrestris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowland Tapir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus wiedii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMargay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePuma concolor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus geoffroyi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeoffroy's Cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanthera onca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaguar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus pardinoides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiger Cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus colocolo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePampas Cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcelot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHerpailurus yagouaroundi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaguarundi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLontra longicaudis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeotropical Otter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGalictis cuja\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLesser Grison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEira barbara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTayra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNasua nasua\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth American Coati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProcyon cancrivorus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrab-eating Raccoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConepatus chinga\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Hog-nosed Skunk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLycalopex gymnocercus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePampa Fox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCerdocyon thous\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrab-eating Fox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEuphractus sexcinctus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYellow Armadillo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChaetophractus villosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge Hairy Armadillo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDasypus novemcinctus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNine-banded Armadillo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDasypus mazzai\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYepes\u0026rsquo;s Mulita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHydrochoerus hydrochaeris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapybara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGalea leucoblephara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Cavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDasyprocta variegata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgouti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyrmecophaga tridactyla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGiant Anteater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTamandua tetradactyla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollared Anteater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSapajus cay\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAzara\u0026rsquo;s Capuchin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\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 \u003c/p\u003e \u003cp\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\u003eRelative and cumulative frequencies expressed in %, of mammal records across four geographic systems: Northern and Southern, Forest and Productive area, as well as the combined total. Relative frequency (RF) refers to the percentage of records represented by each species within the total records for each system, as well as for the entire study region. Cumulative frequency (CF) indicates the progressive sum of relative frequencies, with species ordered from most to least abundant in the pooled dataset, showing their cumulative contribution. In bold highlight the species included in the most explanatory subset for each system.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eNorthern (Salta-Jujuy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eSouthern (Tucum\u0026aacute;n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eProductive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eProductive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDasyprocta variegata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e34,48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20,63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEira barbara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9,62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e30,64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35,12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCerdocyon thous\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9,77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e28,86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8,58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e12,61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSylvilagus brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3,55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e56,22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLycalopex gymnocercus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e50,83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e75,87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64,77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDidelphis albiventris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e17,93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e71,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubulo gouazoubira\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7,28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7,02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e78,27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeopardus pardalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12,02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82,84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePecari tajacu\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4,35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86,48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTapirus terrestris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5,76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal count of records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe observed that mammals in the Yungas occupy or seek their thermal niche, generally ranging between 11 and 21\u0026deg;C (thermal activity window) across seasons. In forested areas, activity curves were typically bimodal during the warmest months, with peaks concentrated at night, whereas in colder months, activity tended to be unimodal and centered around daytime hours. In contrast, in the productive matrix, activity followed a bimodal pattern year-round, with increased activity at dusk during winter and at dawn during summer. These patterns suggest that mammals flexibly adjust their temporal activity to track suitable thermal conditions, modulated by both season and landscape context.\u003c/p\u003e \u003cp\u003eThe observed structure in the activity\u0026ndash;environmental thermal space indicates compensatory behavioral thermal adjustment at the extremes of the purported optimal thermal interval, whereby activity shifts toward conditions closer to the optimal range. In contrast, within the thermoneutral interval, activity closely tracks ambient temperatures without detectable bias. During warmer months, many species increase nocturnal activity, whereas in cooler periods activity is more frequently concentrated during daytime or crepuscular hours. Together, these patterns support the hypothesis that species modulate their temporal activity to maintain thermal conditions near their preferred niche.\u003c/p\u003e \u003cp\u003eMost mammals recorded in our study were primarily nocturnal, supporting the idea that nocturnality is an ancestral trait in the group (Heesy and Hall, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), however various pressures are pushing mammals towards nocturnality. Increasing scientific research suggests that human disturbances\u0026mdash;ranging from urban development and agriculture to lethal and nonlethal activities\u0026mdash;are driving a global increase in nocturnal activity across numerous mammal species (Gaynor et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although we did not find sufficient evidence to classify any species as strictly crepuscular, predictive expectations suggest that such species, if present, may alternate between morning and evening peaks depending on the season. Among the photographed mammals, ten focal species accounted for the majority of detection events across both seasonal periods, highlighting their ecological relevance and suitability as indicators of temporal activity shifts.\u003c/p\u003e \u003cp\u003eWithin the nocturnal species, opossums present well-developed eyes, displaying features that reflect specialization to scotopic vision such as the tapetum lucidum, a membrane positioned in the back of the eye capable of reflecting the light, thus accentuating the contrast of objects, and improving vision in low light conditions (Hoko\u0026ccedil; et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this work, White-eared opossum displayed a predominantly nocturnal pattern. In winter, however, it was frequently detected during daytime hours, particularly at dusk and early evening, indicating that it is not strictly nocturnal. Conversely, during summer, activity peaked in the early morning rather than at evening twilight, possibly associated with the lowest temperature range during the 24 hours, Tripodi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found \u003cem\u003eD. aurita\u003c/em\u003e associated with the lowest temperatures. This seasonal pattern is clearly visible in separate radar charts for warm and cool periods, illustrating temporal shifts in activity intensity. The Crab-eating Fox, is an omnivorous mesocarnivore with nocturnal and crepuscular habits (Faria-Corr\u0026ecirc;a et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In sites exposed to anthropic pressures, this species showed less nocturnal activity and a higher peak of activity in the early morning (Santos et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In forested areas, activity was bimodal during the warm/rainy season and shifted toward crepuscular and early daytime hours in the dry/temperate season. In productive landscapes, nocturnal activity was reduced, and early morning activity became more prominent, while the overall temporal pattern remained consistent across natural vegetation types. These findings suggest that Crab-eating Fox flexibly adjusts its temporal activity to track its thermal niche and potentially to coexist with competitors or exploit anthropogenic resources, without necessarily becoming more strictly nocturnal in disturbed habitats, consistent with observations from other Neotropical regions (Faria-Corr\u0026ecirc;a et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Albanesi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLowland tapir activity shifted toward nocturnality during warm months, concentrating at night and dawn while remaining virtually absent during the day. Dry season activity was bimodal, peaking at dusk and early night. These results support the thermal stress hypothesis, consistent with other Neotropical studies where tapirs avoid heat through nocturnal shifts (Foerster \u0026amp; Vaughan \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Medici \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ayala \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Burs et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such patterns might also reflect seasonal resource shifts or lower nocturnal human disturbance.\u003c/p\u003e \u003cp\u003eThe Tapeti is a generalist mammal, commonly living in transition environments among forests, open fields and the edge of watercourses (Reis et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Albanesi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) and like the tapir, tends to be more active at dusk and in the early evening during the winter, when more heat is retained in the ground. It exhibits a major shift in activity towards the night hours in the rainy season. It is possible that in winter many species extend their foraging range, probably associated with a lower food availability, requiring a longer time frame to feed.\u003c/p\u003e \u003cp\u003eAmong cathemeral species, Cox and Gaston (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) state that cathemeral richness is highest in regions with high environmental seasonality and in mountainous regions, and additionally, their activity may increase following habitat fragmentation. In this work we find the Gray Brocket deer using all hours of the day and night in summer, but making greater use of the afternoon and evening/dusk in winter; however, this pattern was not as pronounced and did not show significant differences. Morello et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) observed a behavior that avoids the exposition to the highest temperatures during the snap time, which can reach up to 45\u0026deg;C in the summer time (Morello et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, this species is considered the most ecologically flexible neotropical deer, surviving in anthropogenic landscapes (Ferreguetti et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For instance, the species has been recorded in human-modified landscapes, including \u003cem\u003eEucalyptus\u003c/em\u003e plantations, sugarcane crops, and isolated forest remnants immersed into agricultural matrix (Black-D\u0026eacute;cima et al. 2010; Magioli et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Records of activity patterns were consistent with literature, which mentions changes in the feeding behaviour of Gray Brocket deer according to seasonality. During the spring and summer seasons, is mainly found during the twilight periods, with individuals primarily active during dawn and dusk (Rivero et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pautasso et al. 2008; Barrientos and Maffei \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Noss et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ferreguetti et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; de Oliveira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grotta Neto et al. 2019). During autumn and winter, it would use all daily hours for foraging, besides dawn and dusk, something reported previously in other small ruminants (Putman \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). This behavioral strategy, which prioritizes quality over quantity, would be responsible for the rhythm of activity previously described, which invests a lot of time in food selection (Richard and Juli\u0026aacute; \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Pampas fox inhabits primarily open environments and exhibits significant ecological and behavioral plasticity, even in human-disturbed landscapes (Lucherini and Luengos Vidal \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Goros\u0026aacute;bel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vidal et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our findings show a strong association between this species and the production system, with activity shifting toward morning twilight in summer and evening in winter. While the fox exploits abundant rodent prey within these sugarcane fields, nocturnal prey behavior and harvesting schedules likely restrict its use of the area to nighttime and specific seasons (Busch et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiurnal species, such as the tayra and agouti, supported our hypothesis by shifting activity toward twilight hours to mitigate thermal stress while remaining within 'illuminated activity' periods. Beyond these limits, activity is presumably reduced due to high physiological or adaptive costs (Bennie et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For instance, Cid et al. (2015) observed D. azarae in the Brazilian Pantanal shifting from a single activity core on cold days to a bimodal pattern on hot days, compensating for midday reductions with afternoon bouts to maintain a constant activity range. This positive correlation between temperature and \u003cem\u003eDasyprocta spp.\u003c/em\u003e activity aligns with several studies (G\u0026oacute;mez et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Suselbeek et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Garc\u0026iacute;a-Restrepo et al. 2019; Mendoza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, while food quality remains a critical driver (Bourgoin et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), the tayra\u0026rsquo;s reliance on areas near water, which offer cooler microclimates, likely offsets the metabolic demands of its diurnal lifestyle (Bianchi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McNab \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, these seasonal shifts represent a behavioral modulation mechanism for tracking a specific thermal niche, where species dynamically reconfigure their daily activity to remain within a favorable 11\u0026ndash;21\u0026deg;C window. Overall, our findings suggest that time acts as a manageable resource to mitigate thermal stress and minimize physiological costs. The strong correlation between temperature and activity patterns across the assemblage indicates that thermal availability is a primary structuring force in the Southern Andean Yungas. Our findings suggest that as global temperatures rise, the availability of thermal refugia, such as dense forest cover and riparian corridors, will become critical for mammalian persistence. Without these buffered microclimates, species may be forced into an increased reliance on nocturnality to escape heat stress. This temporal compression not only limits diurnal foraging opportunities but also increases the risk of new competitive interactions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eResearch funding\u003c/h2\u003e \u003cp\u003eThis research did not receive funding.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A. and D.S. wrote the main manuscript text , prepared figures . All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was conducted within the framework of a collaborative agreement between the Instituto de Biodiversidad Neotropical (IBN, Conicet-UNT) and the Fundaci\u0026oacute;n ProYungas. The primary objective of this agreement is to generate scientific information to strengthen decision-making processes in both public and private spheres. We are grateful to the Fundaci\u0026oacute;n ProYungas for granting us access to their camera trap database, and to Luciana Cristobal for producing the map. Finally, this research would not have been possible without the support of the National Scientific and Technical Research Council (CONICET) and the Universidad Nacional del Tucum\u0026aacute;n (UNT).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbanesi SA, C\u0026aacute;ceres R, Novillo A, Bellis LM (2026) The importance of riparian forests as corridors for mammals in a productive landscape of the Yungas. J Nat Conserv 91:127213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbanesi SA, Jayat JP, Alejandro B (2016) Patrones de actividad de mam\u0026iacute;feros de medio y gran porte en el pedemonte de Yungas del noroeste argentino. Mastozoolog\u0026iacute;a neotropical 23:335\u0026ndash;358\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyala GMC (2003) Monitoreo de Tapirus terrestris en el Izozog (Cerro Cortado) Mediante el Uso de Telemetr\u0026iacute;a como Base para un Plan de Conservaci\u0026oacute;n. 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Oecologia Australis 27(2):240\u0026ndash;247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4257/oeco.2023.2702.11\u003c/span\u003e\u003cspan address=\"10.4257/oeco.2023.2702.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidal B, Verger L, Nagy GJ (2025) The Pampas Fox (\u003cem\u003eLycalopex gymnocercus\u003c/em\u003e, \u0026lsquo;Zorro Gris Pampeano\u0026rsquo;): An Integrative Review of the Ecological, Health, and Conflict Roles of a Key Mesopredator in Southern South America. Wild 2025, 2, 49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/wild2040049\u003c/span\u003e\u003cspan address=\"10.3390/wild2040049\" 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":false,"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":"mammal-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acth","sideBox":"Learn more about [Mammal Research](http://link.springer.com/journal/13364)","snPcode":"13364","submissionUrl":"https://www.editorialmanager.com/acth/default2.aspx","title":"Mammal Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mammalian activity, Thermal window, Behavioral plasticity, Yungas, Camera trapping, Spatio-temporal segregation.","lastPublishedDoi":"10.21203/rs.3.rs-9369097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9369097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the role of environmental temperature as a primary driver of mammalian activity patterns in the Southern Andean Yungas, utilizing a robust 14-year camera trap dataset (2009\u0026ndash;2023) comprising 573 stations and over 20,000 trap-days. By focusing on detection events of ten focal species alongside hourly temperature data, we find that mammal activity is strongly concentrated within a moderate thermal window of 11\u0026ndash;21\u0026deg;C. This ecological core also embraces the optimal window for the majority of the recorded assemblage. Species exhibit considerable behavioral plasticity, temporally shifting their daily activity to track this thermal niche across seasons: during warm months, activity increases during nocturnal and crepuscular periods to avoid thermal stress, while in cooler months, activity becomes more concentrated in diurnal hours. Furthermore, we found that the landscape matrix mediates these responses; while forest populations show marked seasonal shifts (bimodal in summer, unimodal in winter), populations in productive agricultural areas maintain a bimodal pattern year-round. This suggests that anthropogenic habitats alter the expression of thermoregulatory behaviors. Our results highlight a complex interplay between temperature and time, where species-specific responses\u0026mdash;ranging from the nocturnal shift of the Lowland Tapir (\u003cem\u003eTapirus terrestris\u003c/em\u003e) to the crepuscular adjustments of the Crab-eating Fox (\u003cem\u003eCerdocyon thous\u003c/em\u003e)\u0026mdash;facilitate spatio-temporal niche segregation. These findings establish thermal availability as a fundamental structuring force of temporal ecology in subtropical montane systems, providing critical insights for forecasting species resilience under climate change and habitat fragmentation.\u003c/p\u003e","manuscriptTitle":"Linking Thermal Availability to Mammal Activity Patterns through Long-Term Camera Trap Monitoring in the Southern Andean Yungas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 09:36:27","doi":"10.21203/rs.3.rs-9369097/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"213834469471645201919676486344340390512","date":"2026-05-16T16:54:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-16T05:55:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267308609223466551999957716612833558575","date":"2026-05-15T16:13:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52684324899155977312181444300858504439","date":"2026-05-14T16:54:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T23:45:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179198704204898492648853644822123399963","date":"2026-04-30T21:52:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211698221738959873142678155255378087943","date":"2026-04-13T18:59:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T05:43:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-12T22:30:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-12T22:30:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mammal Research","date":"2026-04-09T13:03:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mammal-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acth","sideBox":"Learn more about [Mammal Research](http://link.springer.com/journal/13364)","snPcode":"13364","submissionUrl":"https://www.editorialmanager.com/acth/default2.aspx","title":"Mammal Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"74b1bf45-182f-430e-9034-036c269f8fa9","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"213834469471645201919676486344340390512","date":"2026-05-16T16:54:22+00:00","index":35,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-16T05:55:24+00:00","index":34,"fulltext":""},{"type":"reviewerAgreed","content":"267308609223466551999957716612833558575","date":"2026-05-15T16:13:59+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"52684324899155977312181444300858504439","date":"2026-05-14T16:54:59+00:00","index":32,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T23:45:00+00:00","index":26,"fulltext":""},{"type":"reviewerAgreed","content":"179198704204898492648853644822123399963","date":"2026-04-30T21:52:54+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T09:36:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 09:36:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9369097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9369097","identity":"rs-9369097","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}