Wild divergence: how adaptable are the Molina's hog-nosed skunk and the South American coati to landscape changes in the highly neglected Uruguayan Savannah?

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Jordani Dutra, Maria João Ramos Pereira, Flávia Tirelli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4307305/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding how species respond to changes in their environment is crucial for effective conservation efforts, particularly in vulnerable ecoregions like the Uruguayan Savannah in Brazil and Uruguay. Here, we focused on two often overlooked species, the South American coati (Nasua nasua) and Molina's hog-nosed skunk (Conepaptus chinga), deploying 79 cameras across 15 areas in both the Brazilian and Uruguayan sectors of the ecoregion. Using occupancy models, we investigated the influence of different land cover types (forests, grasslands, farmland, and silviculture) and human density on the presence of these species. We tested the activity pattern uniformity for each species. We looked at the landscape transition history. Furthermore, we also generated innovative occupancy maps to better understand and guide policies and actions for these species. The coati occupancy exhibited a positive relation with forest areas but a negative correlation with grasslands and crop farming areas (p<0.05). The skunk presented a positive response to grassland and crop farming areas but a negative response to forest areas (p<0.05). The South American coati occupancy was estimated at approximately 0.141 (0.041 – 0.420), while for Molina’s hog-nosed skunks, it was 0.377 (0.200 – 0.610). Despite the coati's use of open areas, it demonstrated a stronger association with natural forests than altered landscapes. In contrast, Molina's hog-nosed skunk displayed adaptability, persisting in altered environments. In conclusion, our findings underscore the urgency of prioritizing conservation efforts for coatis, while highlighting the skunk's resilience to landscape alterations. This knowledge can guide targeted conservation plans for these species in threatened ecoregions. Conepatus chinga daily activity patterns grasslands Nasua nasua occupancy Uruguayan Savannah Figures Figure 1 Figure 2 Figure 3 Introduction The temperate grasslands across the globe have experienced transformation into agropastoral areas due to human activities, driven by a combination of distinctive phytosociological features and economic factors (MEA 2005; Suttie et al. 2005). This, coupled with cultural influences, has resulted in a noticeable disparity in conservation awareness compared to environments such as forests or coral reefs (Henwood 2010). Occupying approximately 8% of the Earth's surface (White et al. 2000), temperate grasslands face heightened anthropogenic changes as they present a crucial role in delivering various ecosystem services (ESs) essential for human welfare (MEA 2005; Bengtsson et al. 2019), as well as their accessibility for human settlement (Suttie et al. 2005). The degradation of these natural grasslands significantly contributes to the decline in native species populations, with cropland farming, in particular, impacting mammal abundance, diversity, and persistence in grasslands and savannas worldwide (Sala et al. 2000; Lima et al. 2018; Wait et al. 2018). The Uruguayan Savannah, an ecoregion encompassing the southern region of the Brazilian state of Rio Grande do Sul and the entirety of Uruguay (Olson et al. 2001), assumes paramount importance owing to the array of ecosystem services it provides (Modernel et al. 2016). While predominantly grassland, it also harbors forests, rocky outcrops, wetlands, and diverse vegetation (Hasenack et al. 2010; Bergamin et al. 2024). Unfortunately, the natural grasslands, traditionally used for cattle raising, are being rapidly replaced by soybean fields and monocultures of Eucalyptus and Pinus , posing a significant threat to biodiversity (Baldi and Paruelo 2008; Esteves et al. 2017). This ecoregion presents great importance to botanical and animal studies due to its biodiversity with different areas being highlighted as important to conservation (Bilenca and Miñarro 2004; Loyola et al. 2009). Of particular concern is Leopardus munoai , an endemic felid at high risk of extinction due to those landscape changes (Nascimento et al. 2021; Tirelli et al. 2021). Indeed, land use change emerges as the primary threat to the survival of 91% of small carnivores facing extinction, with 85% specifically attributing this threat to agricultural activities (Marneweck et al. 2021). The comprehensive review by Marneweck et al (2021) highlights the observation that certain small carnivores exhibit resistance and can stay abundant in altered environments. However, a deeper understanding of the factors influencing the ecology of these resilient small carnivorans necessitates more species-specific and site-specific studies. In the context of the Uruguayan Savannah, there are approximately 16 species of carnivorans (Queirolo 2016). This article will delve into the occupancy and activity of two of them: the South American coati ( Nasua nasua ) and the Molina's hog-nosed skunk ( Conepatus chinga ). The South American coati faces threats in Rio Grande do Sul and is a conservation priority in Uruguay (Marques et al. 2002; Soutullo et al. 2013). Notably, it seems to exhibit some adaptability in response to anthropogenic alteration (Lima et al. 2021; Dutra et al. 2023). However, limited research on coatis in this ecoregion underscores the need for a comprehensive understanding. The Molina's hog-nosed skunk, primarily a nocturnal and omnivorous species that eats mostly invertebrates, encounters distinct challenges, notably falling victim to frequent roadkill incidents, although it seems to thrive in altered environments (Bianchin 2011; Peters et al. 2011; de Oliveira et al. 2013; Corrêa et al. 2017). Population trends for the species remain debatable, with varying assessments globally and regionally (Kasper et al. 2013; Emmons et al. 2016), emphasizing the importance of detailed studies in different contexts. Here, we conducted an extensive camera trap survey covering a significant portion of the Uruguayan Savannah. Our study, using occupancy models, focuses on understanding how a threatened species (the South American coati) and a seemingly resistant species (the Molina’s hog-nosed skunk) respond to anthropogenic modifications in the landscape. We expect nuanced responses to human density, natural forests, and altered landscapes like crop farming and silviculture. To aid in species conservation, we generated predictive maps for species occupancy across a substantial portion of the Uruguayan Savannah. This research gives valuable insights into the strategies employed by these species to persist in the face of altered grasslands, ultimately supporting effective conservation measures. Materials And Methods Study area We established a network of 90 camera trap sites across 15 locations within the Uruguayan Savannah between 2019 and 2022. However, our surveying efforts in Brazil faced challenges due to equipment shortages and pandemic-related constraints caused by COVID-19. Consequently, we conducted surveys in various phases: from October 2019 to January 2020 (three areas), January 2020 to May 2020 (three areas), October 2021 to October 2022 (one area), July 2021 to October 2021 (one area), and May 2022 to August 2022 (one area). In Uruguay, our surveys spanned from September 2020 to January 2021. Of the 15 surveyed areas, 10 in Brazil and five areas in Uruguay, 13 were located on privately-owned ranches, while two were within rural properties situated inside a protected area known as Área de Proteção Ambiental do Ibirapuitã (SISBIO: 71166-1). In each area, we set six camera trap sites strategically placed across three distinct habitat types: two in forests, two in natural grasslands, and two in farming areas. Cameras were positioned approximately 30 cm above ground level and deployed without the use of bait. Operating continuously in video mode (10-second clips at 3-second intervals), they recorded activity 24 hours a day. For a more detailed overview of each area access Fig.A1 of the Supplementary Material. Occupancy modelling Site occupancy models offer valuable insights into understanding how specific variables influence the occupancy of a species, aiding in the strategic prioritization of areas for conservation efforts (Calderón et al. 2022). We employed single-season single-species occupancy models to estimate two critical parameters: the probability of detection (p) and the probability of occupancy (ψ) (MacKenzie et al. 2002). To structure the data for each species, we organized records per occasion and site, along with the camera trap operating history and associated variables per camera trap site. For analytical purposes, we defined an occasion as a five-day interval, a strategy aimed at minimizing the prevalence of zero values in the dataset (Mena and Yagui 2019; Dutra et al. 2023). We limited our analysis to the initial 50 days of sampling for each camera. Given the diversity in camera models used in the survey, we introduced the concept of camera trigger time to account for heterogeneity. Additionally, we incorporated the Julian Date as a variable to address potential temporal variations between sites. Human density, ranging from 0 to 10,000 km², was derived from a map provided by the Center for International Earth Science Information Network - CIESIN - and Columbia University (2018). All other land cover variables were obtained from the MapBiomas Pampa Sudamericano Project (MapBiomas Pampa Sudamericano Project 2021). In the assessment of landscape variables, we systematically tested each variable within different buffer sizes (250m, 500m, and 1000m) and selected the optimal buffer size for each variable using the Akaike information criteria adjusted to small samples (AICc). Detailed information about each variable and our predictions can be found in Table 1. Before model execution, we conducted a thorough evaluation of variable correlations through the Spearman correlation test. Variables exhibiting a correlation coefficient exceeding 0.6 or falling below -0.6 were excluded from the analysis (Table A1 and A2 in the Supplementary Material for details). The process of selecting the detection and the occupancy models involved a sequential approach. We initiated with a goodness-of-fit test using a global model that incorporated all variables. The generated c-value (inflation factor) from this test was then used to refine the selection of the detection models. Subsequently, the chosen detection models were integrated into the occupancy models (Burnham and Anderson 2002; MacKenzie and Bailey 2004). In the selection process of both the detection and the occupancy models, we employed the QAICc (Quasi-Akaike information criteria, adjusted for small samples) criterion, following the guidelines of Burnham and Anderson (2002), it was always selected models with ΔQAICc < 2. These analyses were done in the "unmarked" package (Fiske and Chandler 2011) within the R program version 4.3.1 (R Development Core Team 2019). As a supplementary contribution to the discussion, we presented the naïve occupancy value for each species. This value was calculated by dividing the number of sites with at least one detection by the total number of sites. Prediction of occupancy in non-surveyed areas Following a similar scheme from da Costa and Ramos Pereira (2022) we used the above-mentioned occupancy models to extrapolate and predict occupancy in non-surveyed regions. All models with ΔQAICc < 2 were incorporated into the extrapolation process. To manage computing constraints and maintain the parsimony of our prediction, we standardized the resolution of the prediction map to 500m. We predicted exclusively the surveyed ecological ecosystems defined by Hasenack et al. (2010), within the Uruguayan Savannah (2010). Consequently, out of the total ~336,406 km² area of the Uruguayan Savannah, we predicted occupancy for 158,291 km². With our 500m grid, each map comprised 642,297 pixels, each assigned a corresponding occupancy value. We used the “landscapemetrics”, “raster”, “rgdal”, and “sp” R packages (Pebesma et al. 2012; Bivand et al. 2015; Hijmans et al. 2015; Hesselbarth et al. 2019). Transition History Understanding that a species' presence or absence may be influenced by historical land use changes rather than current environmental conditions, we incorporated a temporal dimension into our analysis. We used the coordinates of our data points and examined the land use transition map from the MapBiomas Pampa project spanning 2000 to 2019 (MapBiomas Pampa Sudamericano Project 2021). Activity Pattern For the activity pattern, we used data with at least a 1h interval to avoid registering the same individual too many times (Soria-Díaz et al. 2016). In the absence of available data specific to the Uruguayan Savannah, we characterized the activity patterns of the two focal species. Before the analysis, we corrected the local hour to the sun time using the “overlap” R package (Nouvellet et al. 2012; Meredith and Ridout 2014). Focusing on the spring-summer period from September 22 to March 20, we employed the Rayleigh spatial test for uniformity (Zar 1974). The analyses were conducted using the "circular", an R package (Lund and Agostinelli 2017). Results The overall camera trap sampling effort in this study amounted to 5,620 trap/nights and the records came from 79 specific sites. Several of our primary sites experienced incidents involving the theft of cameras or malfunctions therein. Occupancy modelling The coati's total number of detections was 34, while the Molina’s hog-nosed skunk was 55. Before any modeling, the coati's naïve occupancy stood at 0.126, while the Molina’s hog-nosed skunk's was 0.316. For each species, we developed 48 models, ultimately selecting seven occupancy models for the coati and ten for the skunk (Table 2). The complete array of models is available in the supplementary material (Tables A4 and A5). The estimated occupancy probability for the coati was 0.141 (0.041 – 0.420 CI) across our sampling units, whereas Molina’s hog-nosed skunk showed an occupancy of 0.377 (0.200 – 0.610 CI). The coati exhibited a significant response to the percentage of forest, crop farming and grassland, being positively influenced by the first variable and negatively by the latter two. Conversely, Molina’s hog-nosed skunk responded oppositely, exhibiting a negative correlation with forest and a positive association with grassland and crop farming (see Table 3 for detailed results). Prediction of occupancy for other areas Regarding the prediction of occupancy in non-sampled areas, the extrapolation of results to different parts of the Uruguayan Savannah revealed occupancy values ranging from 0.031 to 0.888 for the coati and 0.058 to 0.478 for Molina’s hog-nosed skunk (Fig. 2). Activity pattern We obtained 58 registers to the South American coati and 72 to the Molina’s hog-nosed skunk to the temporal analysis. Both species demonstrated non-uniform patterns, with the South American coati following a diurnal pattern (Rayleigh-Z 0.6401 , p-value < 0.001), and Molina’s hog-nosed skunk exhibiting a nocturnal pattern (Rayleigh-Z 0.6166 , p-value < 0.001) (Fig.3). Discussion In the face of expanding farming and silviculture within the Uruguayan Savannah, it is crucial to comprehend how different species respond to these landscape alterations. Our extensive camera trap survey, covering various environments in two South American countries, provides unprecedented insights. Occupancy dynamics: contrasting responses of Molina’s hog-nosed skunk and South American coati The Molina’s hog-nosed skunk was detected in nearly twice as many sites as the South American coati, suggesting that it is much more common in the Uruguayan Savannah (Queirolo 2016). Despite the coati's association with forests, the sampling areas are located at the limit of its distribution, potentially leading to lower density and population numbers in these areas. While our study did not reveal significant differences in the effects of silviculture on both species' occupancy, a notable observation from the maps indicates a correlation between lower occupancy areas for both species and regions undergoing silviculture, particularly in Uruguay. Our findings highlight a positive influence of forest cover on coati occupancy, contradicting a prior study with a distinct analytical approach (Lima et al. 2021). However, our results align with earlier research linking the coati to forested environments (Gompper and Decker 1998; Mena and Yagui 2019). In the predominantly grassland Uruguayan Savannah, especially in the less forested eastern portion where our sampling was focused, forested areas seem to play a more restrictive role compared to a primarily forest-dominated environment like the Atlantic Forest. Coati occupancy demonstrated a negative correlation with the percentage of grassland, a novel result indicating the species' preference for forested habitats. Molina’s hog-nosed skunk occupancy exhibited a positive correlation with the percentage of grassland, aligning with traditional knowledge about the species (Macdonald et al. 2018). The negative impact of forests on their occupancy may be due to the species favoring grasslands, potentially for defensive reasons against predators that climb trees. Additionally, the Molina’s hog-nosed skunk showed a positive correlation with the percentage of farming, echoing previous studies in the Uruguayan Savannah, and highlighting its adaptability to human-altered environments, even suburban areas (de Oliveira et al. 2013). These results are curious, considering the general reduction of mammal abundance in grasslands with expanding croplands (Lima et al. 2018). Molina’s hog-nosed skunk's prevalence in roadkill incidents in the Brazilian Uruguayan Savannah adds another layer to its complex relationship with human activities (Peters et al. 2011; Corrêa et al. 2017), especially given the association of grassland maintenance with cattle grazing and controlled fires (Andrade et al., 2024). Although our study did not explore prey availability, Molina’s hog-nosed skunk is known to prefer locations with higher potential prey abundance, as was shown in the Argentinian pampas (Castillo et al. 2012), besides the prominent muzzle, this species has claws and well-developed forelimbs to excavate the soil in search of fossorial small invertebrates (Donadio et al. 2001). In Rio Grande do Sul state, the Molina’s hog-nosed is omnivorous with a predominance of insects, among the insects in its diet, the order Coleoptera is the most common and abundant throughout all year (Peters et al. 2011). Peters et al (2011) also mention that many of the Coleoptera are considered agricultural pests, and have their abundance related to drought and expansion of cultivated areas which may explain its use of crop farms in the Uruguayan Savannah. There is no data if in Uruguayan Savannah the Molina’s hog-nosed skunk selects its habit in function of prey availability or other soil characteristics but is something worth investigating. These occupancy patterns align with changes in landscape cover, with coatis predominantly appearing in more pristine sites, and Molina’s hog-nosed skunk demonstrating adaptability to grassland environments and resilience to potential landscape degradation. The activity pattern does not differ from other areas (Donadio et al. 2001; Bianchi et al. 2016) the coati is mostly diurnal, and Molina’s hog-nosed skunks are nocturnal. We did not have enough data to make a more in-depth analysis regarding the activity. In other regions, both species displayed the capacity to alter their activity because of the anthropogenic influence (Galvez et al. 2021; Castillo and Lucherini 2022; Dutra et al. 2023) Predicting occupancy in unsurveyed areas The predicted coati occupancy was higher in the northeast portion of our study area (Fig. 3), specifically in the Serra do Sudeste . This region, overlapping with a forested area of the Uruguayan Savannah, is also where most of the previous detections of coatis were known (Trigo et al. 2013). The Serra do Sudeste stands out as part of the Uruguayan Savannah harboring more natural forests due to high humidity and temperature, while the eastern portion predominantly features riparian forests surrounded by grasslands (Bergamin et al. 2024). Brazil has stringent legislation protecting riparian forests, designating these areas as protected zones, while Uruguay's regulations are more context-dependent (Sparovek et al. 2011; Zarza et al. 2022). Despite the Uruguayan Savannah having a smaller proportion of forest areas, coatis show increased occupancy probabilities within these specific forested regions. The coati's ability to use secondary forests and to potentially travel long distances, coupled with its diet, suggests it may play a role in seed dispersion and the preservation of these forests, warranting further dietary studies within the Uruguayan Savannah ecoregion (Alves-Costa and Eterovick 2007). While Molina's hog-nosed skunk does not exhibit high occupancy values compared to the South American coati, it shows more uniform values, capable of occupying most analyzed areas of the Uruguayan Savannah. Indeed, a recent study lists the Uruguayan Savannah as highly suitable for this species (Castillo and Caruso 2024). Temperature, a factor not considered in our ecoregion-scale analysis, may play a crucial role in modeling the skunk's distribution, deeming its inclusion in future studies necessary, especially considering climate change effects (Castillo et al. 2015; Castillo and Caruso 2024). The coati's predicted occupancy is higher in the eastern portion, where more forests are present, while the skunk's predicted occupancy is higher in the western portion. The IUCN reports a decreasing population trend for the skunk, though our previous studies question this idea, especially in Brazil (Kasper et al. 2013). Molina's hog-nosed skunk is frequently encountered by the local populations in the Uruguayan Savannah, often finding a place in traditional songs, making it a potential focal point for educational initiatives. Despite being a common species, it receives insufficient attention from science and conservation, highlighting the need for more research due to its probable interactions with a wide range of plants and animals (Gaston and Fuller 2007, 2008). Special attention is drawn to silviculture, where the correlation, though not significant, appears in most top occupancy models. Indeed, maps for both species show low occupancy in areas corresponding to tree monocultures. This warrants careful follow-up, especially given the potential expansion of these plantations. In Uruguay, the forest sector has grown above the national average in recent years; also, in Rio Grande do Sul, there was a notable 30% increase in the silviculture area in the 2000s (Bencke 2009; Olmos and Pienika 2022). Mesocarnivore conservation within the Uruguayan Savannah’s changing landscape When examining the historical land cover, a distinct pattern emerges: the coati exclusively appeared in locations consistently marked as natural within the data frame, whereas the Molina's hog-nosed skunk displayed a more diverse transition history (Table 4). The skunk was prevalent in grassland sites that retained their natural state, but intriguingly, it also occupied areas where grasslands had been converted to crop farming. Additionally, Molina's hog-nosed skunk was found in forest sites that remained as such, suggesting a greater ability compared to the coati to cross a potentially unsuitable matrix to reach these areas. These findings underscore the exceptional adaptability of Molina's hog-nosed skunks, particularly when juxtaposed with the coati, a species already acknowledged for its resilience in the face of anthropogenic changes. The Uruguayan Savannah is considered at least vulnerable, but some authors mention that it may be more threatened, as highlighted by Dinerstein et al. (1995) and Loyola et al.(2009), which emerges as a critical focus in our study. Our research underscores the significance of riparian forests and hillside forests, protected by law in Brazil, in supporting species like the South American coati. Despite being perceived as rare in the ecoregion, our findings reveal the coati's as still resisting in the Uruguayan Savannah, but only in preserved sites. Overall, in alignment with prior research, Molina's hog-nosed skunks exhibit a preference for open grassland areas, yet our study showcases their adaptability to altered environments. In contrast, there are endemic carnivorans within this ecoregion face a heightened risk of extinction due to the diminishing natural grasslands, as indicated by Tirelli et al. (2021). In fact, a disclaimer must be made about a concern that arose from our data. Our initial intention was to focus on four species of the Musteloidea superfamily (Macdonald et al. 2018), the two species evaluated in this study, the South American coati and Molina’s Hog-nosed skunk, but also the lesser grison ( Galicts cuja ) and the crab-eating raccoon ( Procyon cancrivorus ). Our initial expectation was to retrieve reasonable numbers to at least analyze Molina’s hog-nosed skunk and the crab-eating raccoon. But contrary to our expectations we lacked the number of records to conduct spatial and temporal analyses on the crab-eating raccoon. This may potentially be attributed to random effects; however, such an explanation seems unlikely. One of our designated study areas aligns with the location examined by one of the authors in a previous study (Tirelli et al. 2019). Despite differences in sampling efforts, this raises concerns, particularly given that our study encompassed diverse locations. It is conceivable that, in recent years, the crab-eating raccoon population has become more responsive to anthropogenic changes within the Uruguayan Savannah. This inference is supported by the species' heightened sensitivity to such alterations in the Atlantic Forest, as compared to the South American coati (Dutra et al. 2023). Another possibility is the spread of diseases like rabies and others, the crab-eating raccoon is known to carry antibodies for those but is not well know the effect of these diseases on this species (Cheida 2012). Rabies and canine distemper are diseases that play a role in controlling the populations of the northern raccoon ( Procyon lotor ) (Zeveloff 2002). This highlights the urgency of conducting studies on similar diseases in the crab-eating raccoon. In the near future, we aim to focus specifically on the ecology of the crab-eating raccoon, and there is an urge for other researchers to explore this subject further, underscoring the need for continued investigations involving this species and other fauna in the Uruguayan Savannah. Declarations Acknowledgments We would like to thank the landowners who received us and supported our research. Special thanks to Marcelo Oliveira, Mateus Zimmer, and Felipe Peters for help during the field campaigns in Brazil, and Diego Queirolo and Santiago Turcato in Uruguay. We would like to thank Cintia da Costa for helping with the occupancy extrapolation method. Author contributions JD – conceptualization, methodology, fieldwork, forma analyses, writing- original draft; MJRP – conceptualization, methodology, writing – review & editing, and supervision; FPT – conceptualization, methodology, fieldwork, funding acquisition, review & editing, supervision. Funding information This work was carried out with the support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Financing Code 001. The project was partially funded by The Rufford Foundation, project code 32609-1. JD received an MSc scholarship from CAPES. MJRP was supported by a Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) productivity grant. FT received a grant from PNPD/CAPES. The funding sources had no involvement in the study design, collection of data, or analysis. Declaration of competing interest The authors declare no competing interests. Data availability Data will be made available on request. References Alves-Costa CP, Eterovick PC (2007) Seed dispersal services by coatis (Nasua nasua, Procyonidae) and their redundancy with other frugivores in southeastern Brazil. 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Mammalian Biology 99:128–135. https://doi.org/10.1016/j.mambio.2019.11.003 Tirelli FP, Trigo TC, Queirolo D, et al (2021) High extinction risk and limited habitat connectivity of Muñoa’s pampas cat, an endemic felid of the Uruguayan Savanna ecoregion. J Nat Conserv 62:. https://doi.org/10.1016/j.jnc.2021.126009 Trigo TC, Fontoura-Rodrigues ML, Kasper CB (2013) Carnívoros continentais. In: WEBER M de M, Roman C, Cáceres NC (eds) Mamíferos do Rio Grande do Sul. Editora UFSM, Santa Maria, Brazil. Editora da UFSM, Santa Maria - Rio Grande do Sul, pp 343–376 Wait KR, Ricketts AM, Ahlers AA (2018) Land‐use change structures carnivore communities in remaining tallgrass prairie. J Wildl Manage 82:1491–1502. https://doi.org/10.1002/jwmg.21492 White RP, Murray S, Rohweder M, et al (2000) Grassland ecosystems. World Resources Institute Washington, DC, USA Zar JH (1974) Probabilities of rayleigh’s test statistics for circular data. Behavior Research Methods & Instrumentation 6:450. https://doi.org/10.3758/BF03200403 Zarza R, Cal A, Formoso D, et al (2022) First delimitation and land-use assessment of the riparian zones at Uruguayan Pampa. Ecol Inform 71:. https://doi.org/10.1016/j.ecoinf.2022.101781 Zeveloff SI (2002) Raccoons: a natural history. UBC Press Tables Table 1 List of variables used in the models, the code used in models, and our prediction. Variable Code Source and description Predicted effect in the occupancy of the South American coati Effect in the occupancy of the Molina’s hog-nosed skunk Occupancy variables Percentage of forest for Percentage of natural forest area inside the buffer. It was extracted from the MapBiomas Pampa project A positive effect in the coati occupancy as it is a more forested related species A negative effect in the coati occupancy as it is a more forested related species Percentage of grassland gra Percentage of natural grassland inside the buffer A negative relation with coati occupancy as it depends on the forests A positive relation with the skunk occupancy at it is a classical grassland species Percentage of crop farming cro Percentage of crop farming inside the buffer. In the summer months, this variable corresponds mostly to soybean and rice plantation areas. In winter months exotic forage pasture A negative relation with coati occupancy as it dependent on forests Despite being able to survive in modified areas we expect a negative effect on the occupancy of the skunk Percentage of silviculture sil Percentage of silviculture inside the buffer A negative effect in the coati as we think that an artificial forest will not be used by the coati A negative correlation as the skunk is a classical grassland inhabitant Human density hum A human density ranger from 0 to 10,000 per km². Source A neutral correlation as previous studies have found both a negative and positive relation for the coati A neutral correlation, despite the species being detected in suburban areas we don’t think that will be able to survive in areas with high human density Detection variables Julian date julian The correspondent ordinal date to the first day of the five-day sequence Used to control the time differences between sites. Camera trigger time trigger The time the camera takes to start recording. Times are taken from the manufacturer's manual. Values range from 0.15s to 1.2s Used to control the heterogeneity of equipment. Faster cameras should detect more individuals. Table 2 Best models that follow the criteria of ΔQAICc >2. QAICc Quasi Akaike information criteria adjusted; ModelLik: model likehood, QAICcW: weight of the model; quasi log-like value of QAICc models; Cum. Wt: cumulative weight of the models. Model K QAICc ΔQAICc ModelLik QAICcW Quasi.LL Cum.Wt South American coati p(.) Ψ(for) 4 131.407 0.000 1.000 0.097 -61.433 0.097 p(.) Ψ(for + sil) 5 131.706 0.299 0.861 0.083 -60.442 0.180 p(.) Ψ(gra + cro + sil) 6 132.404 0.996 0.608 0.059 -59.619 0.239 p(julian) Ψ(for) 5 132.456 1.048 0.592 0.057 -60.817 0.296 p(julian) Ψ(for + sil) 6 132.692 1.285 0.526 0.051 -59.763 0.347 p(.) Ψ(hum + for + sil) 6 133.099 1.692 0.429 0.042 -59.966 0.388 p(.) Ψ(hum + for) 5 133.353 1.946 0.378 0.037 -61.266 0.425 Molina’s hog-nosed skunk p(.) Ψ(for) 4 141.441 0.000 1.000 0.087 -66.450 0.087 p(.) Ψ(gra + cro) 5 141.841 0.400 0.819 0.071 -65.510 0.159 p(.) Ψ(for + sil) 5 142.384 0.943 0.624 0.054 -65.781 0.213 p(julian) Ψ(for) 5 142.609 1.167 0.558 0.049 -65.894 0.262 p(julian) Ψ(gra + cro) 6 142.842 1.401 0.496 0.043 -64.838 0.305 p(.) Ψ(.) 3 143.073 1.632 0.442 0.039 -68.377 0.344 p(.) Ψ(flo + cro) 5 143.237 1.796 0.407 0.036 -66.208 0.379 p(.) Ψ(gra) 4 143.266 1.824 0.402 0.035 -67.363 0.414 p(julian) Ψ(flo + sil) 6 143.426 1.985 0.371 0.032 -65.130 0.446 p(.) Ψ(flo + gra) 5 143.439 1.998 0.368 0.032 -66.309 0.479 Table 3 Average estimates of the effect of the variables in the occupancy ( Ψ ) and detection (p) for each species. We also present standard error (SE), Z test value (Z), and p-value, which is considered significant when < 0.05 (*) p (julian) Ψ (hum) Ψ (for) Ψ (gra) Ψ (cro) Ψ (sil) South American Coati Estimate 2.30 -0.47 0.89 -1.32 -1.29 -8.60 Std. Error 1.63 0.55 0.35 0.52 0.60 7.79 z value 1.41 0.86 2.55 2.55 2.16 1.10 p-value 0.16 0.39 0.01* 0.01* 0.03* 0.27 Molina’s hog-nosed skunk Estimate 1.71 – -1.06 1.24 0.91 -0.61 Std. Error 0.99 – 0.41 0.46 0.40 0.43 z value 1.72 – 2.57 2.67 2.30 1.40 p-value 0.08 – 0.01* 0.01* 0.02* 0.16 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4307305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294547199,"identity":"f5a87d52-0a25-49cb-8b05-97e2c0148931","order_by":0,"name":"Jordani Dutra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACxgYGBgkgAtEMDB+AmI2daC1sQM4MkBZmImySAGsFamHmAXEJaWFu73144+cOC9kN93sffrb5tU2ej5mB8cPHHDwO6zlubNl7RsJ4wzF2Y+ncvtuGbcwMzJIzt+HRMiONTYK3TSJxwzE2BuncntuMQC1szLwEtEj+hWhh/m3Zc9ueKC3SUFvYpBl+3E4krKXnGLO1bJuE8cxjaWyWvQ23k9uYGZvx+sWwvY3x5tu2Otm+w8eYb/z4c9t2fnvzwQ8f8WlpQLGzDUw2YFMJB/Ko3D94FY+CUTAKRsEIBQDDsE1VLj/n0gAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":true,"prefix":"","firstName":"Jordani","middleName":"","lastName":"Dutra","suffix":""},{"id":294547200,"identity":"b1d9a55c-54a8-4d06-b9a2-1a115a865d62","order_by":1,"name":"Maria João Ramos Pereira","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"João Ramos","lastName":"Pereira","suffix":""},{"id":294547201,"identity":"13b082cf-157a-49e1-9eb8-edbef19afe2e","order_by":2,"name":"Flávia Tirelli","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Flávia","middleName":"","lastName":"Tirelli","suffix":""}],"badges":[],"createdAt":"2024-04-22 16:42:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4307305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4307305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55300330,"identity":"99c1c65b-e17c-4cb1-bd46-af4315fb7ee4","added_by":"auto","created_at":"2024-04-25 11:54:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2308937,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area. a; Location of the Uruguayan Savannah in South America. b; Uruguayan Savannah with Rio Grande Sul state and Uruguay border. c; Uruguayan Savannah and the different types of ecosystems that were surveyed.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4307305/v1/444e9e25515fb378ba7e4952.png"},{"id":55300332,"identity":"2ccaed93-ce22-4c73-9ee0-c974b75f6bc9","added_by":"auto","created_at":"2024-04-25 11:54:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4098624,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the result of the extrapolation data of the South American coati and Molina’s Hog-Nosed Skunk.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4307305/v1/5bf7750adfca5018ef5c961b.png"},{"id":55300331,"identity":"309b9ef5-4477-4fe2-bd6b-0cf15acd0e78","added_by":"auto","created_at":"2024-04-25 11:54:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431540,"visible":true,"origin":"","legend":"\u003cp\u003eCircular graphs. a; the activity pattern of the South American coati. b; the activity pattern of the Molina’s hog-nosed skunk.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4307305/v1/dde0e3cd2671cabae6790b4c.png"},{"id":55759146,"identity":"2db460d5-0c54-45a9-b31c-980711dcaed7","added_by":"auto","created_at":"2024-05-02 18:16:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1730749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4307305/v1/b719522b-5d4a-403b-80ed-f8bf284d014f.pdf"},{"id":55300334,"identity":"74f78c4e-c116-4c0e-9595-61e09493595c","added_by":"auto","created_at":"2024-04-25 11:54:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15423756,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4307305/v1/f3f7bcf68bf0dea43e0399f6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wild divergence: how adaptable are the Molina's hog-nosed skunk and the South American coati to landscape changes in the highly neglected Uruguayan Savannah?","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The temperate grasslands across the globe have experienced transformation into agropastoral areas due to human activities, driven by a combination of distinctive phytosociological features and economic factors (MEA 2005; Suttie et al. 2005). This, coupled with cultural influences, has resulted in a noticeable disparity in conservation awareness compared to environments such as forests or coral reefs (Henwood 2010). Occupying approximately 8% of the Earth\u0026apos;s surface (White et al. 2000), temperate grasslands face heightened anthropogenic changes as they present a crucial role in delivering various ecosystem services (ESs) essential for human welfare (MEA 2005; Bengtsson et al. 2019), as well as their accessibility for human settlement (Suttie et al. 2005). The degradation of these natural grasslands significantly contributes to the decline in native species populations, with cropland farming, in particular, impacting mammal abundance, diversity, and persistence in grasslands and savannas worldwide (Sala et al. 2000; Lima et al. 2018; Wait et al. 2018).\u003c/p\u003e\n\u003cp\u003eThe Uruguayan Savannah, an ecoregion encompassing the southern region of the Brazilian state of Rio Grande do Sul and the entirety of Uruguay (Olson et al. 2001), assumes paramount importance owing to the array of ecosystem services it provides (Modernel et al. 2016). While predominantly grassland, it also harbors forests, rocky outcrops, wetlands, and diverse vegetation (Hasenack et al. 2010; Bergamin et al. 2024). Unfortunately, the natural grasslands, traditionally used for cattle raising, are being rapidly replaced by soybean fields and monocultures of \u003cem\u003eEucalyptus\u003c/em\u003e and \u003cem\u003ePinus\u003c/em\u003e, posing a significant threat to biodiversity (Baldi and Paruelo 2008; Esteves et al. 2017). This ecoregion presents great importance to botanical and animal studies due to its biodiversity with different areas being highlighted as important to conservation (Bilenca and Mi\u0026ntilde;arro 2004; Loyola et al. 2009). Of particular concern is \u003cem\u003eLeopardus munoai\u003c/em\u003e, an endemic felid at high risk of extinction due to those landscape changes (Nascimento et al. 2021; Tirelli et al. 2021). Indeed, land use change emerges as the primary threat to the survival of 91% of small carnivores facing extinction, with 85% specifically attributing this threat to agricultural activities (Marneweck et al. 2021). The comprehensive review by Marneweck et al (2021) highlights the observation that certain small carnivores exhibit resistance and can stay abundant in altered environments. However, a deeper understanding of the factors influencing the ecology of these resilient small carnivorans necessitates more species-specific and site-specific studies. In the context of the Uruguayan Savannah, there are approximately 16 species of carnivorans (Queirolo 2016). This article will delve into the occupancy and activity of two of them: the South American coati (\u003cem\u003eNasua nasua\u003c/em\u003e) and the Molina\u0026apos;s hog-nosed skunk (\u003cem\u003eConepatus chinga\u003c/em\u003e). The South American coati faces threats in Rio Grande do Sul and is a conservation priority in Uruguay (Marques et al. 2002; Soutullo et al. 2013). Notably, it seems to exhibit some adaptability in response to anthropogenic alteration (Lima et al. 2021; Dutra et al. 2023). However, limited research on coatis in this ecoregion underscores the need for a comprehensive understanding. The Molina\u0026apos;s hog-nosed skunk, primarily a nocturnal and omnivorous species that eats mostly invertebrates, encounters distinct challenges, notably falling victim to frequent roadkill incidents, although it seems to thrive in altered environments (Bianchin 2011; Peters et al. 2011; de Oliveira et al. 2013; Corr\u0026ecirc;a et al. 2017). Population trends for the species remain debatable, with varying assessments globally and regionally (Kasper et al. 2013; Emmons et al. 2016), emphasizing the importance of detailed studies in different contexts.\u003c/p\u003e\n\u003cp\u003eHere, we conducted an extensive camera trap survey covering a significant portion of the Uruguayan Savannah. Our study, using occupancy models, focuses on understanding how a threatened species (the South American coati) and a seemingly resistant species (the Molina\u0026rsquo;s hog-nosed skunk) respond to anthropogenic modifications in the landscape. We expect nuanced responses to human density, natural forests, and altered landscapes like crop farming and silviculture. To aid in species conservation, we generated predictive maps for species occupancy across a substantial portion of the Uruguayan Savannah. This research gives valuable insights into the strategies employed by these species to persist in the face of altered grasslands, ultimately supporting effective conservation measures.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established a network of 90 camera trap sites across 15 locations within the Uruguayan Savannah between 2019 and 2022. However, our surveying efforts in Brazil faced challenges due to equipment shortages and pandemic-related constraints caused by COVID-19. Consequently, we conducted surveys in various phases: from October 2019 to January 2020 (three areas), January 2020 to May 2020 (three areas), October 2021 to October 2022 (one area), July 2021 to October 2021 (one area), and May 2022 to August 2022 (one area). In Uruguay, our surveys spanned from September 2020 to January 2021. Of the 15 surveyed areas, 10 in Brazil and five areas in Uruguay, 13 were located on privately-owned ranches, while two were within rural properties situated inside a protected area known as \u003cem\u003e\u0026Aacute;rea de Prote\u0026ccedil;\u0026atilde;o Ambiental do Ibirapuit\u0026atilde;\u003c/em\u003e (SISBIO: 71166-1). In each area, we set six camera trap sites strategically placed across three distinct habitat types: two in forests, two in natural grasslands, and two in farming areas. Cameras were positioned approximately 30 cm above ground level and deployed without the use of bait. Operating continuously in video mode (10-second clips at 3-second intervals), they recorded activity 24 hours a day. For a more detailed overview of each area access Fig.A1 of the Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOccupancy modelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSite occupancy models offer valuable insights into understanding how specific variables influence the occupancy of a species, aiding in the strategic prioritization of areas for conservation efforts (Calder\u0026oacute;n et al. 2022). We employed single-season single-species occupancy models to estimate two critical parameters: the probability of detection (p) and the probability of occupancy (\u0026psi;)\u0026nbsp;(MacKenzie et al. 2002).\u003c/p\u003e\n\u003cp\u003eTo structure the data for each species, we organized records per occasion and site, along with the camera trap operating history and associated variables per camera trap site. For analytical purposes, we defined an occasion as a five-day interval, a strategy aimed at minimizing the prevalence of zero values in the dataset (Mena and Yagui 2019; Dutra et al. 2023). We limited our analysis to the initial 50 days of sampling for each camera. Given the diversity in camera models used in the survey, we introduced the concept of camera trigger time to account for heterogeneity. Additionally, we incorporated the Julian Date as a variable to address potential temporal variations between sites. Human density, ranging from 0 to 10,000 km\u0026sup2;, was derived from a map provided by the Center for International Earth Science Information Network - CIESIN - and Columbia University (2018). All other land cover variables were obtained from the MapBiomas Pampa Sudamericano Project (MapBiomas Pampa Sudamericano Project 2021).\u003c/p\u003e\n\u003cp\u003eIn the assessment of landscape variables, we systematically tested each variable within different buffer sizes (250m, 500m, and 1000m) and selected the optimal buffer size for each variable using the Akaike information criteria adjusted to small samples (AICc). Detailed information about each variable and our predictions can be found in Table 1. Before model execution, we conducted a thorough evaluation of variable correlations through the Spearman correlation test. Variables exhibiting a correlation coefficient exceeding 0.6 or falling below -0.6 were excluded from the analysis (Table A1 and A2 in the Supplementary Material for details).\u003c/p\u003e\n\u003cp\u003eThe process of selecting the detection and the occupancy models involved a sequential approach. We initiated with a goodness-of-fit test using a global model that incorporated all variables. The generated c-value (inflation factor) from this test was then used to refine the selection of the detection models. Subsequently, the chosen detection models were integrated into the occupancy models (Burnham and Anderson 2002; MacKenzie and Bailey 2004).\u003c/p\u003e\n\u003cp\u003eIn the selection process of both the detection and the occupancy models, we employed the QAICc (Quasi-Akaike information criteria, adjusted for small samples) criterion, following the guidelines of Burnham and Anderson (2002), it was always selected models with\u0026nbsp;\u0026Delta;QAICc \u0026lt; 2. These analyses were done in the \u0026quot;unmarked\u0026quot; package (Fiske and Chandler 2011) within the R program version 4.3.1 (R Development Core Team 2019). As a supplementary contribution to the discussion, we presented the na\u0026iuml;ve occupancy value for each species. This value was calculated by dividing the number of sites with at least one detection by the total number of sites.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003ePrediction of occupancy in non-surveyed areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Following a similar scheme from da Costa and Ramos Pereira (2022) we used the above-mentioned occupancy models to extrapolate and predict occupancy in non-surveyed regions. All models with \u0026Delta;QAICc \u0026lt; 2 were incorporated into the extrapolation process. To manage computing constraints and maintain the parsimony of our prediction, we standardized the resolution of the prediction map to 500m. We predicted exclusively the surveyed ecological ecosystems defined by Hasenack et al. (2010), within the Uruguayan Savannah\u0026nbsp;(2010). Consequently, out of the total ~336,406 km\u0026sup2; area of the Uruguayan Savannah, we predicted occupancy for 158,291 km\u0026sup2;. With our 500m grid, each map comprised 642,297 pixels, each assigned a corresponding occupancy value. We used the \u0026ldquo;landscapemetrics\u0026rdquo;, \u0026ldquo;raster\u0026rdquo;, \u0026ldquo;rgdal\u0026rdquo;, and \u0026ldquo;sp\u0026rdquo; R packages (Pebesma et al. 2012; Bivand et al. 2015; Hijmans et al. 2015; Hesselbarth et al. 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransition History\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding that a species\u0026apos; presence or absence may be influenced by historical land use changes rather than current environmental conditions, we incorporated a temporal dimension into our analysis. We used the coordinates of our data points and examined the land use transition map from the MapBiomas Pampa project spanning 2000 to 2019 (MapBiomas Pampa Sudamericano Project 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eActivity Pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the activity pattern, we used data with at least a 1h interval to avoid registering the same individual too many times (Soria-D\u0026iacute;az et al. 2016). In the absence of available data specific to the Uruguayan Savannah, we characterized the activity patterns of the two focal species. Before the analysis, we corrected the local hour to the sun time using the \u0026ldquo;overlap\u0026rdquo; R package (Nouvellet et al. 2012; Meredith and Ridout 2014). Focusing on the spring-summer period from September 22 to March 20, we employed the Rayleigh spatial test for uniformity (Zar 1974). \u0026nbsp;The analyses were conducted using the \u0026quot;circular\u0026quot;, an R package (Lund and Agostinelli 2017).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe overall camera trap sampling effort in this study amounted to 5,620 trap/nights and the records came from 79 specific sites. Several of our primary sites experienced incidents involving the theft of cameras or malfunctions therein.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eOccupancy modelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coati's total number of detections was 34, while the Molina’s hog-nosed skunk was 55. Before any modeling, the coati's naïve occupancy stood at 0.126, while the Molina’s hog-nosed skunk's was 0.316. For each species, we developed 48 models, ultimately selecting seven occupancy models for the coati and ten for the skunk (Table 2). The complete array of models is available in the supplementary material (Tables A4 and A5).\u003c/p\u003e\n\u003cp\u003eThe estimated occupancy probability for the coati was 0.141 (0.041 – 0.420 CI) across our sampling units, whereas Molina’s hog-nosed skunk showed an occupancy of 0.377 (0.200 – 0.610 CI). The coati exhibited a significant response to the percentage of forest, crop farming and grassland, being positively influenced by the first variable and negatively by the latter two. Conversely, Molina’s hog-nosed skunk responded oppositely, exhibiting a negative correlation with forest and a positive association with grassland and crop farming (see Table 3 for detailed results).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of occupancy for other areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the prediction of occupancy in non-sampled areas, the extrapolation of results to different parts of the Uruguayan Savannah revealed occupancy values ranging from 0.031 to 0.888 for the coati and 0.058 to 0.478 for Molina’s hog-nosed skunk (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eActivity pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained 58 registers to the South American coati and 72 to the Molina’s hog-nosed skunk to the temporal analysis. Both species demonstrated non-uniform patterns, with the South American coati following a diurnal pattern (Rayleigh-Z\u003csub\u003e0.6401\u003c/sub\u003e, p-value \u0026lt; 0.001), and Molina’s hog-nosed skunk exhibiting a nocturnal pattern (Rayleigh-Z\u003csub\u003e0.6166\u003c/sub\u003e, p-value \u0026lt; 0.001) (Fig.3).\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003eIn the face of expanding farming and silviculture within the Uruguayan Savannah, it is crucial to comprehend how different species respond to these landscape alterations. Our extensive camera trap survey, covering various environments in two South American countries, provides unprecedented insights.\u003c/p\u003e\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eOccupancy dynamics: contrasting responses of Molina’s hog-nosed skunk and South American coati\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The Molina’s hog-nosed skunk was detected in nearly twice as many sites as the South American coati, suggesting that it is much more common in the Uruguayan Savannah (Queirolo 2016). Despite the coati's association with forests, the sampling areas are located at the limit of its distribution, potentially leading to lower density and population numbers in these areas. While our study did not reveal significant differences in the effects of silviculture on both species' occupancy, a notable observation from the maps indicates a correlation between lower occupancy areas for both species and regions undergoing silviculture, particularly in Uruguay.\u003c/p\u003e\u003cp\u003eOur findings highlight a positive influence of forest cover on coati occupancy, contradicting a prior study with a distinct analytical approach (Lima et al. 2021). However, our results align with earlier research linking the coati to forested environments (Gompper and Decker 1998; Mena and Yagui 2019). In the predominantly grassland Uruguayan Savannah, especially in the less forested eastern portion where our sampling was focused, forested areas seem to play a more restrictive role compared to a primarily forest-dominated environment like the Atlantic Forest. Coati occupancy demonstrated a negative correlation with the percentage of grassland, a novel result indicating the species' preference for forested habitats. Molina’s hog-nosed skunk occupancy exhibited a positive correlation with the percentage of grassland, aligning with traditional knowledge about the species (Macdonald et al. 2018). The negative impact of forests on their occupancy may be due to the species favoring grasslands, potentially for defensive reasons against predators that climb trees. Additionally, the Molina’s hog-nosed skunk showed a positive correlation with the percentage of farming, echoing previous studies in the Uruguayan Savannah, and highlighting its adaptability to human-altered environments, even suburban areas (de Oliveira et al. 2013). These results are curious, considering the general reduction of mammal abundance in grasslands with expanding croplands (Lima et al. 2018). Molina’s hog-nosed skunk's prevalence in roadkill incidents in the Brazilian Uruguayan Savannah adds another layer to its complex relationship with human activities (Peters et al. 2011; Corrêa et al. 2017), especially given the association of grassland maintenance with cattle grazing and controlled fires (Andrade et al., 2024). Although our study did not explore prey availability, Molina’s hog-nosed skunk is known to prefer locations with higher potential prey abundance, as was shown in the Argentinian pampas (Castillo et al. 2012), besides the prominent muzzle, this species has claws and well-developed forelimbs to excavate the soil in search of fossorial small invertebrates (Donadio et al. 2001). In Rio Grande do Sul state, the Molina’s hog-nosed is omnivorous with a predominance of insects, among the insects in its diet, the order Coleoptera is the most common and abundant throughout all year (Peters et al. 2011). Peters et al (2011) also mention that many of the Coleoptera are considered agricultural pests, and have their abundance related to drought and expansion of cultivated areas which may explain its use of crop farms in the Uruguayan Savannah. There is no data if in Uruguayan Savannah the Molina’s hog-nosed skunk selects its habit in function of prey availability or other soil characteristics but is something worth investigating.\u003c/p\u003e\u003cp\u003eThese occupancy patterns align with changes in landscape cover, with coatis predominantly appearing in more pristine sites, and Molina’s hog-nosed skunk demonstrating adaptability to grassland environments and resilience to potential landscape degradation.\u003c/p\u003e\u003cp\u003eThe activity pattern does not differ from other areas (Donadio et al. 2001; Bianchi et al. 2016)\u0026nbsp; the coati is mostly diurnal, and Molina’s hog-nosed skunks are nocturnal. We did not have enough data to make a more in-depth analysis regarding the activity. In other regions, both species displayed the capacity to alter their activity because of the anthropogenic influence (Galvez et al. 2021; Castillo and Lucherini 2022; Dutra et al. 2023)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePredicting occupancy in unsurveyed areas\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe predicted coati occupancy was higher in the northeast portion of our study area (Fig. 3), specifically in the \u003cem\u003eSerra do Sudeste\u003c/em\u003e. This region, overlapping with a forested area of the Uruguayan Savannah, is also where most of the previous detections of coatis were known (Trigo et al. 2013). The \u003cem\u003eSerra do Sudeste\u003c/em\u003e stands out as part of the Uruguayan Savannah harboring more natural forests due to high humidity and temperature, while the eastern portion predominantly features riparian forests surrounded by grasslands (Bergamin et al. 2024). Brazil has stringent legislation protecting riparian forests, designating these areas as protected zones, while Uruguay's regulations are more context-dependent (Sparovek et al. 2011; Zarza et al. 2022). Despite the Uruguayan Savannah having a smaller proportion of forest areas, coatis show increased occupancy probabilities within these specific forested regions. The coati's ability to use secondary forests and to potentially travel long distances, coupled with its diet, suggests it may play a role in seed dispersion and the preservation of these forests, warranting further dietary studies within the Uruguayan Savannah ecoregion (Alves-Costa and Eterovick 2007).\u003c/p\u003e\u003cp\u003eWhile Molina's hog-nosed skunk does not exhibit high occupancy values compared to the South American coati, it shows more uniform values, capable of occupying most analyzed areas of the Uruguayan Savannah. Indeed, a recent study lists the Uruguayan Savannah as highly suitable for this species (Castillo and Caruso 2024). Temperature, a factor not considered in our ecoregion-scale analysis, may play a crucial role in modeling the skunk's distribution, deeming its inclusion in future studies necessary, especially considering climate change effects (Castillo et al. 2015; Castillo and Caruso 2024). The coati's predicted occupancy is higher in the eastern portion, where more forests are present, while the skunk's predicted occupancy is higher in the western portion. The IUCN reports a decreasing population trend for the skunk, though our previous studies question this idea, especially in Brazil (Kasper et al. 2013). Molina's hog-nosed skunk is frequently encountered by the local populations in the Uruguayan Savannah, often finding a place in traditional songs, making it a potential focal point for educational initiatives. Despite being a common species, it receives insufficient attention from science and conservation, highlighting the need for more research due to its probable interactions with a wide range of plants and animals (Gaston and Fuller 2007, 2008).\u003c/p\u003e\u003cp\u003eSpecial attention is drawn to silviculture, where the correlation, though not significant, appears in most top occupancy models. Indeed, maps for both species show low occupancy in areas corresponding to tree monocultures. This warrants careful follow-up, especially given the potential expansion of these plantations. In Uruguay, the forest sector has grown above the national average in recent years; also, in Rio Grande do Sul, there was a notable 30% increase in the silviculture area in the 2000s (Bencke 2009; Olmos and Pienika 2022).\u003c/p\u003e\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eMesocarnivore conservation within the Uruguayan Savannah’s changing landscape\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWhen examining the historical land cover, a distinct pattern emerges: the coati exclusively appeared in locations consistently marked as natural within the data frame, whereas the Molina's hog-nosed skunk displayed a more diverse transition history (Table 4). The skunk was prevalent in grassland sites that retained their natural state, but intriguingly, it also occupied areas where grasslands had been converted to crop farming. Additionally, Molina's hog-nosed skunk was found in forest sites that remained as such, suggesting a greater ability compared to the coati to cross a potentially unsuitable matrix to reach these areas. These findings underscore the exceptional adaptability of Molina's hog-nosed skunks, particularly when juxtaposed with the coati, a species already acknowledged for its resilience in the face of anthropogenic changes. The Uruguayan Savannah is considered at least vulnerable, but some authors mention that it may be more threatened, as highlighted by Dinerstein et al. (1995) and Loyola et al.(2009), which emerges as a critical focus in our study. Our research underscores the significance of riparian forests and hillside forests, protected by law in Brazil, in supporting species like the South American coati. Despite being perceived as rare in the ecoregion, our findings reveal the coati's as still resisting in the Uruguayan Savannah, but only in preserved sites.\u003c/p\u003e\u003cp\u003eOverall, in alignment with prior research, Molina's hog-nosed skunks exhibit a preference for open grassland areas, yet our study showcases their adaptability to altered environments. In contrast, there are endemic carnivorans within this ecoregion face a heightened risk of extinction due to the diminishing natural grasslands, as indicated by Tirelli et al. (2021). In fact, a disclaimer must be made about a concern that arose from our data. Our initial intention was to focus on four species of the Musteloidea superfamily (Macdonald et al. 2018), the two species evaluated in this study, the South American coati and Molina’s Hog-nosed skunk, but also the lesser grison (\u003cem\u003eGalicts cuja\u003c/em\u003e) and the crab-eating raccoon (\u003cem\u003eProcyon cancrivorus\u003c/em\u003e). Our initial expectation was to retrieve reasonable numbers to at least analyze Molina’s hog-nosed skunk and the crab-eating raccoon. But contrary to our expectations we lacked the number of records to conduct spatial and temporal analyses on the crab-eating raccoon. This may potentially be attributed to random effects; however, such an explanation seems unlikely. One of our designated study areas aligns with the location examined by one of the authors in a previous study (Tirelli et al. 2019). Despite differences in sampling efforts, this raises concerns, particularly given that our study encompassed diverse locations. It is conceivable that, in recent years, the crab-eating raccoon population has become more responsive to anthropogenic changes within the Uruguayan Savannah. This inference is supported by the species' heightened sensitivity to such alterations in the Atlantic Forest, as compared to the South American coati (Dutra et al. 2023). Another possibility is the spread of diseases like rabies and others, the crab-eating raccoon is known to carry antibodies for those but is not well know the effect of these diseases on this species (Cheida 2012). Rabies and canine distemper are diseases that play a role in controlling the populations of the northern raccoon (\u003cem\u003eProcyon lotor\u003c/em\u003e) (Zeveloff 2002). This highlights the urgency of conducting studies on similar diseases in the crab-eating raccoon. In the near future, we aim to focus specifically on the ecology of the crab-eating raccoon, and there is an urge for other researchers to explore this subject further, underscoring the need for continued investigations involving this species and other fauna in the Uruguayan Savannah.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;We would like to thank the landowners who received us and supported our research. Special thanks to Marcelo Oliveira, Mateus Zimmer, and Felipe Peters for help during the field campaigns in Brazil, and Diego Queirolo and Santiago Turcato in Uruguay. We would like to thank Cintia da Costa for helping with the occupancy extrapolation method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJD\u003c/strong\u003e \u0026ndash; conceptualization, methodology, fieldwork, forma analyses, writing- original draft;\u003cstrong\u003e\u0026nbsp;MJRP\u0026nbsp;\u003c/strong\u003e\u0026ndash; conceptualization, methodology, writing \u0026ndash; review \u0026amp; editing, and supervision; \u003cstrong\u003eFPT\u003c/strong\u003e\u0026ndash; conceptualization, methodology, fieldwork, funding acquisition, review \u0026amp; editing, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out with the support of the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior \u0026ndash; Brazil (CAPES) \u0026ndash; Financing Code 001. The project was partially funded by The Rufford Foundation, project code 32609-1. JD received an MSc scholarship from CAPES. MJRP was supported by a Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq) productivity grant. FT received a grant from PNPD/CAPES. The funding sources had no involvement in the study design, collection of data, or analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlves-Costa CP, Eterovick PC (2007) Seed dispersal services by coatis (Nasua nasua, Procyonidae) and their redundancy with other frugivores in southeastern Brazil. Acta Oecologica 32:77\u0026ndash;92. https://doi.org/10.1016/j.actao.2007.03.001\u003c/li\u003e\n\u003cli\u003eBaldi G, Paruelo JM (2008) Land-Use and Land Cover Dynamics in South American Temperate Grasslands. Ecology and Society 13:art6. https://doi.org/10.5751/ES-02481-130206\u003c/li\u003e\n\u003cli\u003eBencke GA (2009) Diversidade e conserva\u0026ccedil;\u0026atilde;o da fauna dos Campos do Sul do Brasil. In: Pillar VDP, M\u0026uuml;ller SC, Castilhos ZM de S, Jacques AV\u0026Aacute; (eds) Campos Sulinos: Conserva\u0026ccedil;\u0026atilde;o e uso sustent\u0026aacute;vel da biodiversidade. Minist\u0026eacute;rio do Meio Ambiente, Bras\u0026iacute;lia, pp 101\u0026ndash;121\u003c/li\u003e\n\u003cli\u003eBengtsson J, Bullock JM, Egoh B, et al (2019) Grasslands\u0026mdash;more important for ecosystem services than you might think. Ecosphere 10:. https://doi.org/10.1002/ecs2.2582\u003c/li\u003e\n\u003cli\u003eBergamin RS, Molz M, Rosenfield MF, et al (2024) Forests in the South Brazilian Grassland Region. In: Overbeck GE, Pillar VDP, M\u0026uuml;ller SC, Bencke GA (eds) South Brazilian Grasslands. Springer International Publishing, Cham, pp 385\u0026ndash;415\u003c/li\u003e\n\u003cli\u003eBianchi RDC, Olifiers N, Gompper ME, Mour\u0026atilde;o G (2016) Niche partitioning among mesocarnivores in a Brazilian wetland. PLoS One 11:1\u0026ndash;17. https://doi.org/10.1371/journal.pone.0162893\u003c/li\u003e\n\u003cli\u003eBianchin JF (2011) Mastofauna n\u0026atilde;o-voadora do Parque Estadual do Espinilho, Barra do Quara\u0026iacute;, Rio Grande do Sul, Brasil. Revista da Gradua\u0026ccedil;\u0026atilde;o 4:1\u0026ndash;66\u003c/li\u003e\n\u003cli\u003eBilenca D, Mi\u0026ntilde;arro F (2004) Identificaci\u0026oacute;n de \u0026aacute;reas valiosas de pastizal en las pampas y campos de Argentina, Uruguay y sur de Brasil. Fundaci\u0026oacute;n Vida Silvestre Argentina, Buenos Aires\u003c/li\u003e\n\u003cli\u003eBivand R, Keitt T, Rowlingson B, et al (2015) Package \u0026lsquo;rgdal.\u0026rsquo; Bindings for the Geospatial Data Abstraction Library Available online: https://cran r-project org/web/packages/rgdal/index html (accessed on 15 October 2017)\u003c/li\u003e\n\u003cli\u003eBurnham KP, Anderson DR (2002) Model Selection and Inference: A Practical Information-Theoretic Approach, 2nd edn. Springer, New York\u003c/li\u003e\n\u003cli\u003eCalder\u0026oacute;n AP, Louvrier J, Planillo A, et al (2022) Occupancy models reveal potential of conservation prioritization for Central American jaguars. 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UBC Press\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eList of variables used in the models, the code used in models, and our prediction.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003eSource and description\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003ePredicted effect in the occupancy of the South American coati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003eEffect in the occupancy of the Molina\u0026rsquo;s hog-nosed skunk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOccupancy variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003efor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of natural forest area inside the buffer. It was extracted from the MapBiomas Pampa project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003eA positive effect in the coati occupancy as it is a more forested related species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003eA negative effect in the coati occupancy as it is a more forested related species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003egra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of natural grassland inside the buffer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003eA negative relation with coati occupancy as it depends on the forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003eA positive relation with the skunk occupancy at it is a classical grassland species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of crop farming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003ecro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of crop farming inside the buffer. In the summer months, this variable corresponds mostly to soybean and rice plantation areas. In winter months exotic forage pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003eA negative relation with coati occupancy as it dependent on forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003eDespite being able to survive in modified areas we expect a negative effect on the occupancy of the skunk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of silviculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003esil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of silviculture inside the buffer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003eA negative effect in the coati as we think that an artificial forest will not be used by the coati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003eA negative correlation as the skunk is a classical grassland inhabitant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003eHuman density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003ehum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003eA human density ranger from 0 to 10,000 per km\u0026sup2;. Source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003eA neutral correlation as previous studies have found both a negative and positive relation for the coati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003eA neutral correlation, despite the species being detected in suburban areas we don\u0026rsquo;t think that will be able to survive in areas with high human density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDetection variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82758620689655%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.729885057471265%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003eJulian date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003ejulian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003eThe correspondent ordinal date to the first day of the five-day sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.55747126436781%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUsed to control the time differences between sites.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.971264367816094%\" valign=\"top\"\u003e\n \u003cp\u003eCamera trigger time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" valign=\"top\"\u003e\n \u003cp\u003etrigger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.850574712643677%\" valign=\"top\"\u003e\n \u003cp\u003eThe time the camera takes to start recording. Times are taken from the manufacturer\u0026apos;s manual. Values range from 0.15s to 1.2s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.55747126436781%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUsed to control the heterogeneity of equipment. Faster cameras should detect more individuals.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eBest models that follow the criteria of \u0026Delta;QAICc \u0026gt;2. QAICc Quasi Akaike information criteria adjusted; ModelLik: model likehood, QAICcW: weight of the model; quasi log-like value of QAICc models; Cum. Wt: cumulative weight of the models.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"707\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003eQAICc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Delta;QAICc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003eModelLik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003eQAICcW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003eQuasi.LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003eCum.Wt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth American coati\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(for)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e131.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-61.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(for + sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e131.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-60.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.)\u0026nbsp;\u0026Psi;(gra + cro + sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e132.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-59.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(julian) \u0026Psi;(for)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e132.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-60.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(julian) \u0026Psi;(for + sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e132.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-59.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.)\u0026nbsp;\u0026Psi;(hum + for + sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e133.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e1.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-59.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(hum + for)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e133.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e1.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e-61.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolina\u0026rsquo;s hog-nosed skunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(for)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e141.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-66.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(gra + cro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e141.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-65.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(for + sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e142.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-65.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(julian) \u0026Psi;(for)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e142.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-65.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(julian)\u0026nbsp;\u0026Psi;(gra + cro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e142.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-64.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e143.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-68.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(flo + cro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e143.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-66.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(gra)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e143.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-67.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(julian)\u0026nbsp;\u0026Psi;(flo + sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e143.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-65.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.8967468175389%\" valign=\"top\"\u003e\n \u003cp\u003ep(.) \u0026Psi;(flo + gra)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6775106082036775%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.76944837340877%\"\u003e\n \u003cp\u003e143.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.022630834512023%\"\u003e\n \u003cp\u003e1.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.618104667609618%\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335219236209335%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.193776520509195%\"\u003e\n \u003cp\u003e-66.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.486562942008486%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eAverage estimates of the effect of the variables in the occupancy (\u003cem\u003e\u0026Psi;\u003c/em\u003e) and detection (p) for each species. We also present standard error (SE), Z test value (Z), and p-value, which is considered significant when\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"bottom\"\u003e\n \u003cp\u003ep (julian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026Psi; (hum)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026Psi; (for)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026Psi; (gra)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026Psi; (cro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026Psi; (sil)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth American Coati\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e-8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolina\u0026rsquo;s hog-nosed skunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.764227642276424%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032520325203253%\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Conepatus chinga, daily activity patterns, grasslands, Nasua nasua, occupancy, Uruguayan Savannah","lastPublishedDoi":"10.21203/rs.3.rs-4307305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4307305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Understanding how species respond to changes in their environment is crucial for effective conservation efforts, particularly in vulnerable ecoregions like the Uruguayan Savannah in Brazil and Uruguay. Here, we focused on two often overlooked species, the South American coati (Nasua nasua) and Molina's hog-nosed skunk (Conepaptus chinga), deploying 79 cameras across 15 areas in both the Brazilian and Uruguayan sectors of the ecoregion. Using occupancy models, we investigated the influence of different land cover types (forests, grasslands, farmland, and silviculture) and human density on the presence of these species. We tested the activity pattern uniformity for each species. We looked at the landscape transition history. Furthermore, we also generated innovative occupancy maps to better understand and guide policies and actions for these species. The coati occupancy exhibited a positive relation with forest areas but a negative correlation with grasslands and crop farming areas (p\u003c0.05). The skunk presented a positive response to grassland and crop farming areas but a negative response to forest areas (p\u003c0.05). The South American coati occupancy was estimated at approximately 0.141 (0.041 – 0.420), while for Molina’s hog-nosed skunks, it was 0.377 (0.200 – 0.610). Despite the coati's use of open areas, it demonstrated a stronger association with natural forests than altered landscapes. In contrast, Molina's hog-nosed skunk displayed adaptability, persisting in altered environments. In conclusion, our findings underscore the urgency of prioritizing conservation efforts for coatis, while highlighting the skunk's resilience to landscape alterations. This knowledge can guide targeted conservation plans for these species in threatened ecoregions.","manuscriptTitle":"Wild divergence: how adaptable are the Molina's hog-nosed skunk and the South American coati to landscape changes in the highly neglected Uruguayan Savannah?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 11:54:31","doi":"10.21203/rs.3.rs-4307305/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"217636b6-4b0b-4f9a-9410-409739e9a1be","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T18:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 11:54:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4307305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4307305","identity":"rs-4307305","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}