Comparison of the conservation of medium and large-sized mammals in a national park and military area

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Although both regions have positive and negative effects on animal communities, studies comparing their impacts on conservation are limited. We monitored mammalian communities using 21 sensor cameras in a MA (6 sites) and NP (15 sites) located on Gyeryongsan Mountain, South Korea from May to November 2021 to compare the conservation of medium and large-sized mammals between the two regions. Forty-one environmental variables, including anthropogenic and geographical factors, were extracted from different spatial ranges (50, 500, and 1000 m). A linear model and non-metric multidimensional scaling were used to identify the factors influencing community diversity. We also analyzed species habitat type preferences using a multispecies occupancy model and compared temporal activities in the two regions. Species diversity was similar between the two sites, with most animals preferring habitats with lower slopes located at greater distances from human trails. Only the Korean hare ( Lepus coreanus ) preferred the NP habitat of the eight species found. Active periods were similar for the species in both regions, except for differences in some carnivore species. Although not all species were affected by human activity, most preferred the MA over the NP. The carnivores were especially able to flexibly alter their active periods and locations in response to human activities, especially in the NP where human activity was more prevalent. Thus, strategies should be implemented to improve NP conservation success, such as spatial and temporal accessible and inaccessible section separation. Biodiversity conservation camera trapping multispecies occupancy spatiotemporal occupancy conservation planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction National parks (NPs) and military areas (MAs) preserve natural ecosystems from anthropogenic impacts. Human activities are limited in NPs to protect remnant species, whereas MAs indirectly protect habitat by prohibiting non-military activities for reasons of military security (Devictor et al. 2007 ; Arimoro et al. 2020 ); however these areas also negatively impact biodiversity conservation. For example, NPs attract tourists that may destroy fauna and introduce invasive species and certain military activities disturb species habitats (Gese et al. 1989 ; Rogala et al. 2011 ). Despite these conflicting effects, only a few studies have compared their respective impacts on species conservation. Most fauna research comparing NPs and MAs has been limited to a few biota (e.g., invertebrates) or have consisted of separate assessments on their impacts on animal communities. For example, butterfly richness was greater in MAs compared to nature reserves in the Czech Republic, but the number of endangered butterfly species was lower in MA than in nature reserves, with different species composition found in each region (Cizek et al. 2013 ). Additionally, 82% of an additional 76 studied protected areas including NPs effectively maintained their species population due to low rates of habitat loss (Geldmann et al. 2013 ). In fact, MAs have recently been recognized to act as wildlife refuges to a similar degree as NPs. One Brazilian study reported that endangered species that had disappeared in other areas were rediscovered in military zones (Arimoro et al. 2020 ). Similarly, a MA in the Netherlands occupying only 1% of the land base contained 53% of the plant and 61% of the bird species found in the entire country (Lawrence et al. 2015 ). Negative impacts on biodiversity conservation have also been documented in both areas. For example, the carnivorous black kite ( Milvus migrans ) and red kite ( Milvus milvus ) have been negatively affected by human activity in NPs due to their wide home ranges (Sergio et al. 2005 ). Additionally, field signs of large mammals have been found to decrease when more people use NP trails (Zhou et al. 2013 ). In MAs, weapon discharges affect wildlife activities and vegetation and road and building construction alters the land type. Furthermore, noise from military activities deters wildlife from human and wildlife trails (Marshall et al. 2012 ; Lindenmayer et al. 2016 ). For example, the loss of grassland due to military operations has been shown to decrease the flight and hunting activities of the ferruginous hawk ( Buteo regalis ; Andersen et al. 2004 ). In 2021, MAs covered 51.5 km2 or 0.05% of the total area of South Korea, which has been under a truce since 1953 (Jung 2019 ; Ministry of National Defense 2023). In total, 6,168 species and 102 endangered species (38% of the total endangered species in the country) inhabit the demilitarized zone, which is one of the largest MAs in South Korea (Kim 2022 ; Lee et al. 2007 ). In this study, we compare the effects of a NP and MA on the local biota of Gyeryongsan National Park, which includes Gyeryongsan Mountain. The headquarters of the South Korean Armed Forces (Gyeryong-dae) is a MA located on this mountain that has only minor disturbances related to civilian and military training access. Since these two areas are connected, they have similar fauna; however, they differ in their levels of anthropogenic disturbance due to their differing land-use types (i.e., NP or MA). This study compares the conservation of medium and large-sized mammals in the NP and MA of Gyeryongsan National Park to determine differences in the diversity and presence probabilities between the two areas and to determine how species spatiotemporally occupy the NP and MA. 2. Materials and methods 2.1. Study areas Gyeryongsan Mountain (36°21'N, 127°13'E) has an altitude of 847 m and a mean slope of 15.6°. The mean annual temperature is 11°C, with a range of 16℃ to 25°C in summer and − 10℃ to − 1°C in winter. In total, 56% of the annual precipitation of 1,326.6 mm occurs between June and August. The Gyeryongsan Mountain national park (65.335 km2; Fig. 1 ) includes the Gyeryong-dae MA (29.75 km2; Fig. 1 ). A portion of the NP is open to visitors and includes 52.15 km of trails and a total visitor area of 56.05 km: Access to the MA is restricted, but the NP is frequently visited due to the nearby metropolitan city of Daejeon (Korea National Park Service 2021 ). A total of 4,502 species, including four endangered species, inhabit this mountain. The main forest type is broad-leaved, but vegetation differs depending on altitude. In general, Korean red pine ( Pinus densiflora ) and Mongolian oak ( Quercus mongolica ) are located above 650 m, Bakdal birch ( Betula schmidtii pegel ) and palmate maple ( Acer palmatum ) are found at 400–650 m, and Korean red pine ( Pinus densiflora ) and elm-like trees ( Zelkova serrata ) are found at 200–400 m. 2.2. Camera trapping Camera trapping was conducted from May to November 2021 at 21 sites (15 NP and 6 MA sites). Two types of cameras were used: Browning BTC-6PXD (Browning, USA); and Trophy Camera HD (Bushnel, USA). These were installed where wildlife field signs were found (e.g., footprints, scats, or rubbed trees) and were set up for taking three consecutive pictures at 30s intervals over the course of the observation period, for at least on month at each study site (Meek et al. 2013 ). The coordinate information for the cameras was collected using a handheld global positioning system (Garmin 64S, USA). 2.3. Environmental variables influencing medium and large-sized mammals Since each species can be affected by additional environmental factors other than the type of conservation area (NP or MA), 41 environmental variables reported to affect medium and large-sized mammals were collected (Table 1 ). Land-use types (urban, agricultural, forest, grass, wetland, barren, and water areas) were identified based on a land-cover map produced by the Ministry of Environment (2021) and a trail map produced by the Korea Forest Service ( https://www.forest.go.kr ). To determine the most relevant spatial range that could explain the effects of the different variables on the studied species, the area of land use was identified at three different scales: 50, 500, and 1000 m using the buffer tool and calculate geometry dialog box in ArcMap v. 10.5 (Esri, USA; Hong and Joo 2021 ). Distances were measured using the near function of the same program. Landscape variables (e.g., aspect, slope, and altitude) were collected based on digital elevation models produced by the National Geographic Information Institute of Republic of Korea (NGII) (Hong et al. 2020 ). Finally, the sites were classified as NPs or MAs. Table 1 Classifications and related references for the 41 selected variables. Anthropogenic and landscape factors were determined based on their distances to the camera trapping sites and areas at radii of 50, 500, and 1000 m Classification Variable Reference Anthropogenic factors Urban Brocardo et al., 2023 Agriculture Li et al., 2016 Forest Dhakal et al., 2022 Grass Tian et al., 2019 Wetland Lezzi et al., 2020 Barren Lezzi et al., 2020 Trail Zhou et al., 2013 Type of conservation area (NP or MA) Gray et al., 2016 ; Cizek et al., 2013 Landscape Aspect Tian et al., 2019 Slope Tian et al., 2019 Altitude Brocardo et al., 2023 Water areas Dhakal et al., 2022 2.4. Data analysis Linear models (LM) were used to determine the effects of the environmental variables on species diversity. The capture histories for each species at the different study sites were refined to measure species occurrence at the community level. Briefly, photos of species that occurred more than once within a 30 min interval were removed to reduce reporting bias of the same individuals (Farris et al. 2012 ). The species occurrence frequencies and the Shannon diversity indexes (diversity) of the frequencies between species were calculated as follows: Occurrence frequency of species = \(\frac{the number of occurrences}{camera activation period}\) Diversity (H) = \(-\sum [{p}_{i}\times \text{l}\text{n}\left({p}_{i}\right)]\) where p i indicates the proportion of the i th species. Multi-model inferences were used to define the influential variable for diversity (Shannon diversity). The relative ranks of candidate models were determined based on the lowest Akaike's information criterion (AICc), which was corrected for small sample sizes (Anderson and Burnham 2004 ). Collinearity between the selected environmental variables was assessed based on the variance inflation factor (VIF) value, with high collinearity (VIF ≥ 5) variables excluded. The dredge function of the MuMIn package was then executed using all combinations of the environmental variables (Barton 2018 ). Models with AICc < 2 were averaged to globally define the significant variables. The explanatory variables were selected by repeating the process after the variables from the averaged models that were not considered covariates were removed, since they did not show statistical significance (MacNally 2000 ). If the weights of the multi-models did not show competency (weight ≥ 0.8), we considered the averaged model as the most parsimonious (Anderson and Burnham 2004 ). The accuracy of the best fit model was determined based on the R-squared coefficients between the observed and expected diversity of each species. We then performed non-metric multidimensional scaling (nMDS) with 41 environmental factors using the Bray-Curtis coefficient to ordinate the species occurrence frequency between the NP and MA and conducted a Monte Carlo randomization test. To evaluate the taxon similarity between the two regions, a pairwise analysis of similarities (ANOSIM) and a permutational multivariate analysis of variance (PERMANOVA) were used. We used a similarity percentage analysis (SIMPER) to determine which indicator species differentiated the two regions. All multivariate analyses and visualizations were performed using the ggplot2 package in R (version 3.5.0). To define the protective role of each area (NP and MA) at the species level, we applied the multispecies occupancy models in R to estimate the interactions between individual species and mammalian communities in terms of the environmental variables using the Bayesian approach (Mackenzie et al. 2017 ). The significant variables were used as fixed effects based on previous analyses (LM and nMDS) and species as random effects to compare the impacts of the NP and MA in association with the environmental variables. Additionally, a fixed effect was applied to indicate whether the site was located in the NP or the MA to test the second hypothesis. Multispecies occupancy modeling was performed using the community model function in the camtrap package in R (Hubbard et al. 2022 ). The 10 day presence and absence values were divided into 1 bin and the number of iterations, burn-ins, and chains was 1000, 500, and 3, respectively. We compared the occurrence frequencies between the NP and MA to examine the differences in the temporal activity pattern of each species. Camera trap data, including time of capture, was used to create a density plot to depict the distribution of photos taken over 24 hours. The type of conservation area was added as a column of data. Diel activity was measured by comparing and overlapping the activity patterns from all medium and large-sized mammal species that occurred in the NP and MA using kernel density plots. The overlap coefficient was calculated as the proportion of overlap between the two diel activity curves in the NP and MA using the overlap package in R (Meredith et al. 2014 ). The overlap was calculated with 999 bootstraps to obtain a 95% confidence interval. The values ranged from 0 (no overlap) to 1 (complete overlap). 3. Results 3.1. Medium and large-sized mammals inhabiting the NP and MA In total, six families and eight species of medium and large-sized mammals concurrently inhabited the NP and MA. Two endangered species, the leopard cat ( Prionailurus bengalensis ) and yellow-throated marten ( Martes flavigula ), were found in both areas. The species were classified as follows: 3 carnivores (Siberian weasel ( Mustela sibirica ), leopard cat ( P. bengalensis ), and yellow-throated marten ( M. flavigula )); 3 omnivores (wild boar ( Sus scrofa ), raccoon dog ( Nyctereutes procyonoides ), and badger ( Meles leucrus )); and two herbivores (water deer ( Hydropotes inermis ) and Korean hare ( Lepus coreanus )). The mean diversity between the NP (1.1 ± 0.1) and MA (1.2 ± 0.1) was similar (Fig. 2 ). Among the study sites, the F10 site in the NP had the greatest diversity, whereas the C61 site in the NP showed the least diversity (Fig. 2 a). Three sites (F8 and F10 in the NP, and the HBC site in the MA) had the highest number of species (7 species) (Fig. 2 b). The E1 site in the MA showed the lowest number of species (2 species). 3.2. Differences between the NP and MA at the community level Among the 1,023 models evaluated, the best fit model was based on a linear model with slope used as the significant predictor (Fig. 3 ). A high slope (coefficient = − 0.14 ± 0.06) indicated low mammal community diversity (r2 = 0.23, adjusted r2 = 0.19). However, type of conservation area (NP of MA), as a factor that could compare for degree of conservation between two regions, was not selected. The medium and large-sized mammal communities divided between the NP and MA were clustered; thus, no significant differences were noted in the nMDS analysis. The variable loading results showed that the slope (r2 = 0.25; p = 0.09) and distance from the trail (TRAIL_DIST; r2 = 0.28; p = 0.05) contributed significantly to the ordination axis 2 (Fig. 4 ). Two factors explained that the mammalian communities depended more on the geographical or anthropogenic components of their habitats than on differences in the type of conservation area (NP or MA). The ANOSIM results supported the position of the slope and distance from the trail as determined by ordination. There was a greater dissimilarity between the two factors than within them (R = − 0.09; p = 0.72). Badgers ( M. leucrus ), raccoon dogs ( N. procyonoides ), and yellow-throated martens ( M. flavigula ) appeared more frequently at greater distances from trails with lower slopes, which contrasted with the water deer patterns ( H. inermis ). The results of PERMANOVA supported differences in the mammalian community composition between the NP and MA (pseudo-F = 0.80; p = 0.589), but these were not significant. The slope and distance from the trail separated the communities. The SIMPER analysis showed that raccoon dogs ( N. procyonoides ; 19.3%) were a significant indicator species differentiating the two regions (Table 2 ). Table 2 Similarity percentage (SIMPER) analysis results of the species compositions for the mammal communities in the national park (NP) and military area (MA). The four species with the highest contributions are provided Species Frequency in the NP Frequency in the MA Contribution (%) p Water deer 0.100 0.239 28.6 0.330 Badger 0.236 0.128 23.2 0.996 Wild boar 0.117 0.185 20.0 0.690 Raccoon dog 0.065 0.135 19.3 0.028 3.3. Differences in conservation effect between the NP and MA by species The preference of each species for the slope or distance from the trail, which were selected as significant predictors, differed between the LM and nMDS analyses (Fig. 5 a, b). The slope results revealed that three of the eight species (i.e., the yellow-throated marten ( M. flavigula ), Korean hare ( L. coreanus ), and leopard cat ( P. bengalensis )) had values on the left in the middle, indicating that they preferred flat areas. Regarding the distance from the trail, the yellow-throated marten ( M. flavigula ) was the only species with values on the right in the middle; thus, yellow-throated martens ( M. flavigula ) preferred to live far from the trail. The preference of each species for inhabiting the NP or the MA differed (Fig. 5 c). Of the eight species, only the Korean hare ( L. coreanus ) showed values on the right in the middle, indicating a preference for the NP. Five species preferred the MA (raccoon dogs ( N. procyonoides ), wild boars ( S. scrofa ), leopard cats ( P. bengalensis ), badgers ( M. leucrus ), and water deer ( H. inermis )). Siberian weasels ( M. sibirica ) and yellow-throated martens ( M. flavigula ) inhabited the two regions similarly. Activity time was similar in both regions, but differed for three of the carnivore species. In particular, the activity diel of the yellow-throated marten ( M. flavigula ; overlap ratio = 0.6; Fig. 6 h), Siberian weasel ( M. sibirica ; overlap ratio = 0.3; Fig. 6 e) and leopard cat ( P. bengalensis ; overlap ratio = 0.38; Fig. 6 c) changed depending on region (Fig. 6 ). The Siberian weasel ( M. sibirica ) was nocturnal in the MA, but was active during both the day and night in the NP (Fig. 6 e). The leopard cat ( P. bengalensis ), was active during the day and night in MA, but nocturnal NP (Fig. 6 c). Four species (badger, ( M. leucrus ), raccoon dog, ( N. procyonoides ), water deer, ( H. inermis ) and wild boar ( S. scrofa )) were documented a similar number of times (overlap ratio of badger = 0.86; raccoon dog = 0.88; water deer = 0.79; wild boar = 0.82) in both areas. 4. Discussion This study provides a comprehensive comparison of the conservation roles of a NP and MA for medium and large-sized mammals. The two connected regions had the same temperate mountain climate and similar forest types. Similar forest communities generally show comparable diversities in forest mammal species, which was also found in this study (Iezzi et al. 2021). Despite their connectivity, most parts of the studied NP and MA were distant from each other. Distribution differences in mammals sensitive to habitat heterogeneity are caused by differences in overall habitat components, which complicates the determination of habitat preference for wildlife in those regions. For this reason, we distinguished the habitat preferences for medium and large-sized mammals. The two regions selected for this study provide a good comparison, since they are located on the same mountain with similar habitats and environments. The LM and nMDS analyses identified that the importance of slope and distance from trail differed depending on the diversity and composition of the mammalian community. Wildlife generally chooses sloped gradients for rearing and hiding infants to utilize the features of inclined planes (Jeong et al. 2021 ). In this study, mammals used flat topographies to move to other habitats. Most wildlife use trails for various purposes, including foraging, movement, and communication with other animals (Hill et al. 2021 ). Herbivorous and omnivorous mammals were less prevalent in the geographical areas of this study; the trail located on a low-slope road in the forest that was frequently accessed by predators and humans had a low abundance of certain herbivores and omnivores. Thus, to maintain a diverse medium and large-sized mammal community, the slope and distance from the trail must be considered. The multispecies occupancy model also suggested that the studied mammalian species preferred different environments depending on the slope and distance from the trail. These geographical factors often provide marking areas for predators. For example, carnivores deposit their scats on a cross road in a forest to improve the likelihood of detection from other animals (Barja et al. 2004 ). Similarly, our results indicate that the yellow-throated marten ( M. flavigula ) and leopard cat ( P. bengalensis ) may use ridges as convenient routes through the mountain for movement; however, mammals prefer to move through gentle-slope areas to other habitats when these are not accessed by humans (Tobler et al. 2018 ). In particular, carnivores, such as the red fox ( Vulpes vulpes ) and ocelot ( Leopardus pardalis ), prefer flat areas but are not found when tourists and hikers use the trail (Harmsen et al. 2010 ; Suzuki et al. 2023). Of the species that preferred a gentle-slope area, yellow-throated martens ( M. flavigula ) avoided trail hikers because they were sensitive to human disturbance. Additionally, some herbivores prefer flat habitats due to food availability. For example, Korean hares ( L. coreanus ) were primarily found in grasslands with lower slopes where they could readily find grasses and tree stems as food resources. Generally, most mammalian species used gentle slopes and trails for multiple purposes, but often avoided the areas used by humans. The multispecies occupancy model showed that the preference for living in the NP or MA differed among the studied species. The Korean hare ( L. coreanus ) only inhabited the NP, but most of the species preferred the MA environment. The distribution of medium and large-sized mammals that are sensitive to human activity decreases as human presence increases. In the Hengduan Mountains of China, the proportion of herbivores significantly decreases as human presence increases and carnivore presence also significantly declines with increased human disturbance (Li et al. 2022 ). To avoid encountering people, mammals use the MA for refuge. When herbivores occur frequently in a region, the frequency of predation increases (Owen-Smith 2015 ). To avoid predators, the Korean hare ( L. coreanus ) might select the most effective area because this species has a smaller home range than other mammalian herbivores. Thus, to activate their natural ranging behavior while avoiding the risk of natural enemies, such as predators and humans, such species could be different their preference between the NP and MA as needed. Although the majority of the temporal activity patterns were similar for the studied species, they differed for carnivores depending on human activity in both areas. The NP has a greater human presence (e.g., tourists and hikers) that is usually active in the morning and before sunset. Some soldiers are also present in the MA around the clock, but their activities occur on a more regular schedule than those of the NP visitors. These human activities affect the timing and areas of carnivore activities. On the Iberian Peninsula, red fox activities ( Vulpes vulpes ) increased in areas far from human residences (Diaz-Ruiz 2016). Similarly, the yellow-throated marten ( M. flavigula ) may move between the NP and MA to avoid humans and maintain their wide home range. For example, their activity density was higher and occurred throughout the day in the MA where human activity was less. The Siberian weasel ( M. sibirica ) and leopard cat ( P. bengalensis ) also displayed flexible behavior patterns between the two study regions, which further supports the conclusion that carnivores are more likely affected by human disturbance. Time use for the medium and large-sized mammals tended to significantly differ due to differences in the degree of anthropogenic disturbance between the two regions. This might relate to a decline in productivity, behavior, abundance and richness of individuals, and community interactions. This tended to be especially true for carnivores, who were more affected by anthropogenic disturbance than the other mammals. Similarly, wolf movements occurred at different times than human activities in regions where human movement was high (Mori, 2020). According to our results, most carnivores preferred the flat areas, such as human trails. The timing of mammal activities was not always affected by human activities, as indicated by the result that most of the studied mammal species preferred the MA. Furthermore, some mammals changed their active periods and space use depending on human activities, which represents the importance of spatiotemporal separation in the NP. Diversity could be increased in the NP by separating the accessibly and inaccessible sections of the park and by using time (i.e., active preiods) in a combined spatiotemporal strategy. The NP was selected by the government to serve as a protected ecosystem with various high-quality services provided to the public, such as ecotourism and animal and ecology education. These activities cause alterations in the timing of animal movements and space usage of mammals according to the proximity of the human activities to mammals. Recently, programs have been implemented to reduce animal stress from anthropogenic sources, such as the restriction of access time and division of available space. Since NP are used for both tourism and ecosystem conservation with the goal of the coexistence of several species, general species habitats and Red List species should be managed by separating the core conservation zones from the MA. Thus, to increase species diversity in NPs, we emphasize the importance of management strategies that separate species habitats into human accessible and restricted zones and times, including the restriction of hiking trail portions. 5. Conclusions Although the characteristics of the studied NP and MA were similar in terms of forest ecosystem conservation, species diversity and the number of species that prefer the habitat were greater in the MA than in the NP. Despite the NP designation as a protected area, high human activity levels cause mammals to avoid certain areas either completely or at times of high human activities, which has a negative effect on conservation. Thus, protected areas require more suitable management to increase diversity, such as through spatial and temporal separation of accessible and inaccessible sections. Declarations Acknowledgments We thank all researchers who helped with the surveys with field assistance. Declaration of Generative AI and AI-assisted technologies in the writing process Authors disclose that no use of generative AI and AI-assisted technologies have been used 396 in the writing process Funding This work was supported by the Sejong Science Fellowship, the National Research Foundation of Korea (NRF) [grant number NRF-2021R1C1C2004162]. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Data curation, investigation, methodology; writing-original draft were performed by [Mihyeon Kim]. The conceptualization, methodology, formal analysis, validation, visualization were performed by [Hyo Gyeom Kim]. The conceptualization, writing – review & editing, supervision, project administration, funding acquisition were performed by [Sungwon Hong]. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. 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Biological Conservation 204:276-283. https://doi.org/10.1016/j.biocon.2016.10.030 Li Z, Song B, Zhao Q, Ding S (2016) Effects of multi-scale landscape heterogeneity on soil meso-and microfaunal communities in typical regions of the lower reaches of the Yellow River. Acta Ecologica Sinica 36:448-455. https://doi.org/10.1016/j.chnaes.2016.10.005 Li X, Hu W, Bleisch WV, Li Q, Wang H, Lu W, sun J, Zhang F, Ti B, Jiang X (2022) Functional diversity loss and change in nocturnal behavior of mammals under anthropogenic disturbance. Conservation Biology 36:e13839. https://doi.org/10.1111/cobi.13839 MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey L, Hines JE (2017) Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier, Amsterdam MacNally R (2000) Regression and model-building in conservation biology, biogeography and ecology: the distinction between–and reconciliation of–‘predictive’ and ‘explanatory’ models. Biodiversity & Conservation 9:655-671. https://doi.org/10.1023/A:1008985925162 Marshall ME, Long AM, Farrell SL, Mathewson HA, Morrison ML, Newnam C, Wilkins RN (2012) Using impact assessment study designs for addressing impacts to species of conservation concern. Wildlife Society Bulletin 36:450-456. https://doi.org/10.1002/wsb.179 Meek PD, Vernes K, Falzon G (2013) On the reliability of expert identification of small-medium sized mammals from camera trap photos. Wildlife Biology in Practice 9:1-19. https://doi.org/10.2461/wbp.2013.9.4 Meredith M, Ridout M, Meredith MM (2014) Package ‘overlap’. Estimates of coefficient of overlapping for animal activity patterns, 3, 1. The Comprehensive R Archive Network. Accessed 1 August 2023 Ministry of National defense Republic of Korea (2023) Statistical YearBook of National defense 2022. Ministry of National defense Republic of Korea, Seoul Mori E, Bagnato S, Serroni P, Sangiuliano A, Rotondaro F, Marchiano V, Cascini V, Poerio L, Ferretti F (2020) Spatiotemporal mechanisms of coexistence in an European mammal community in a protected area of southern Italy. Journal of Zoology 310:232-245. https://doi.org/10.1111/jzo.12743 Ngoprasert D, Lynam A J, Gale G A (2017) Effects of temporary closure of a national park on leopard movement and behaviour in tropical Asia. Mammalian Biology 82:65-73. https://doi.org/10.1016/j.mambio.2016.11.004 Owen‐Smith N (2015) Mechanisms of coexistence in diverse herbivore–carnivore assemblages: demographic, temporal and spatial heterogeneities affecting prey vulnerability. Oikos 124:1417-1426. https://doi.org/10.1111/oik.02218 Rogala JK, Hebblewhite M, Whittington J, White CA, Coleshill J, Musiani M (2011) Human activity differentially redistributes large mammals in the Canadian Rockies National Parks. Ecology and Society 16:16. http://dx.doi.org/10.5751/ES-04251-160316 Sergio F, Blas J, Forero M, Fernández N, Donázar JA, Hiraldo F (2005) Preservation of wide-ranging top predators by site-protection: black and red kites in Donana National Park. Biological conservation 125:11-21. https://doi.org/10.1016/j.biocon.2005.03.002 Suzuki M, Saito MU (2023) Forest road use by mammals revealed by camera traps: a case study in northeastern Japan. Landscape and Ecological Engineering 19:289-296. https://doi.org/10.1007/s11355-023-00544-y Tian C, Liao P-C, Dayananda B, Zhang Y-Y, Liu Z-X, Li J-Q, Yu B, Qing L (2019) Impacts of livestock grazing, topography and vegetation on distribution of wildlife in Wanglang National Nature Reserve, China. Global Ecology and Conservation 20:e00726. https://doi.org/10.1016/j.gecco.2019.e00726 Tobler MW, Anleu RG, Carrillo-Percastegui SE, Santizo GP, Polisar J, Hartley AZ, Goldstein I (2018) Do responsibly managed logging concessions adequately protect jaguars and other large and medium-sized mammals? Two case studies from Guatemala and Peru. Biological Conservation 220:245-253. https://doi.org/10.1016/j.biocon.2018.02.015 Zhou Y, Buesching CD, Newman C, Kaneko Y, Xie Z, Macdonald DW (2013) Balancing the benefits of ecotourism and development: The effects of visitor trail-use on mammals in a Protected Area in rapidly developing China. Biological Conservation 165:18-24. https://doi.org/10.1016/j.biocon.2013.05.007 Additional Declarations No competing interests reported. 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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-3667778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":253490120,"identity":"cf14b27a-1f3f-4155-8628-5c00b4db16fa","order_by":0,"name":"Mihyeon Kim","email":"","orcid":"","institution":"Kyungpook National University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mihyeon","middleName":"","lastName":"Kim","suffix":""},{"id":253490121,"identity":"a53499ab-9cb6-406d-b0e3-45f1ea6331bc","order_by":1,"name":"Hyo Gyeom Kim","email":"","orcid":"","institution":"Ulsan National Institute of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hyo","middleName":"Gyeom","lastName":"Kim","suffix":""},{"id":253490122,"identity":"3fee2c2d-f730-4c26-af2c-800131106a8c","order_by":2,"name":"Sungwon Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCRBRkQDlHSBayxmStTC2kaJFfnbzsYdf56XlGRxgfviB4cw9wloM7hxLN5bdllNscIDNWILhRjERWiRyzKQlt1UkbjjAYMbA8CGBoA4G+RkgLXNAWti/EaeF4UaOmeTHhhygFh6gLTeI0GJwIy1NmuFYWuLMwzzFEgnw0MbrsORjkj9qkhP7jrdv/PDhGDEOAwJmHjAJxERqAMbkD2JVjoJRMApGwcgEAIRgOx7cn7IEAAAAAElFTkSuQmCC","orcid":"","institution":"Kyungpook National University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sungwon","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2023-11-26 14:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3667778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3667778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47384050,"identity":"a0e97b86-3666-49ba-8fb3-05d9a5e3aadb","added_by":"auto","created_at":"2023-11-30 17:10:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279330,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Gyeryongsan Mountain national park near the city of Daejeon-si, South Korea (population: approximately 145 million). Right: Altitudes of various parts of Gyeryongsan Mountain. Higher altitudes are expressed in red. The military area (red line) includes the south of the park (black line). Red dots indicate the 21 camera trap sites\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/5fc8e3c64939496a7e3bf4e9.png"},{"id":47384047,"identity":"c48700a2-53e5-49fe-9a2f-9b81d364d7f9","added_by":"auto","created_at":"2023-11-30 17:10:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":346298,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Mammal diversity at each site. Right: Number of species at each site. Larger circles indicate more species\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/92eab40de134c7135585a883.png"},{"id":47385405,"identity":"4d2c62a2-9034-46d6-aa75-2cd1faef7e7d","added_by":"auto","created_at":"2023-11-30 17:18:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101675,"visible":true,"origin":"","legend":"\u003cp\u003eThe linear relationship between the value of the slope and diversity index for medium and large-sized mammals and the formula representing that relationship. Red line: slope trend. Black dots: values of diversity for each camera site. Adjust R-squared represents the explanatory powers of the slope trends\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/49f0ac62f9bc357222756f24.png"},{"id":47384049,"identity":"6453daaa-ced9-41a8-977b-5b3cb9323d8b","added_by":"auto","created_at":"2023-11-30 17:10:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":184249,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of the mammalian communities affected by the national park (NP; red dots) and military area (MA; green dots) using non-metric multidimensional scaling for 21 study sites. Representative responses along the two fence height ranges (NP and MA) are indicated by red arrows with a 95% statistical confidence. The 95% confidence interval ellipses are displayed within the NP (red dotted line) and the MA (blue dotted line)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/d52682f6a35143650a96f894.png"},{"id":47385404,"identity":"49d1c96f-c5ec-4f77-878a-e954d8cdd171","added_by":"auto","created_at":"2023-11-30 17:18:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82125,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized coefficients and 95% credible intervals for the influences of slope (a), distance from the trail (b), and (c) influence of conservation area based on the Bayesian multispecies occupancy model. Species presence probabilities are expressed as dots according to their preference for the given factor. For example, in (a), if the dot is located directly in the middle, when the value of 0 indicates the center, the species preferred the flat area. A value of 0 indicates that the factor had no effect. In case of (c), 8 species that showed preference for the MA (left diagram) or NP (right) are expressed. The value of 0 indicates a preference to live in the MA; therefore, species with values on the right in the middle preferred the NP over the MA\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/7b0b95baa833f128a666a4af.png"},{"id":47384046,"identity":"e4cb02a0-69df-402d-a62d-fca14811012b","added_by":"auto","created_at":"2023-11-30 17:10:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131697,"visible":true,"origin":"","legend":"\u003cp\u003eThe characterization of temporal activity patterns using the camtrap R package. Kernel density functions were used to express the density of mammal species according to their active periods in the military area (MA; red color) and national park (NP; blue color). Activity overlap is represented by shaded areas in graphs with an estimated coefficient (∆). Confidence intervals from the bootstraps are expressed by lower and upper values near the overlap. The number written near the type of conservation area (MA and NP) is the number of camera data per species that was collected in each area\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/7fb05d24d759a97725dc1d41.png"},{"id":47814074,"identity":"c4afe8cb-c75a-4666-9027-d78dc0ef2289","added_by":"auto","created_at":"2023-12-07 19:37:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1593871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3667778/v1/22af828f-483a-45c6-9c0f-b1d0c3a66366.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of the conservation of medium and large-sized mammals in a national park and military area","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNational parks (NPs) and military areas (MAs) preserve natural ecosystems from anthropogenic impacts. Human activities are limited in NPs to protect remnant species, whereas MAs indirectly protect habitat by prohibiting non-military activities for reasons of military security (Devictor et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Arimoro et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); however these areas also negatively impact biodiversity conservation. For example, NPs attract tourists that may destroy fauna and introduce invasive species and certain military activities disturb species habitats (Gese et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Rogala et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Despite these conflicting effects, only a few studies have compared their respective impacts on species conservation.\u003c/p\u003e \u003cp\u003eMost fauna research comparing NPs and MAs has been limited to a few biota (e.g., invertebrates) or have consisted of separate assessments on their impacts on animal communities. For example, butterfly richness was greater in MAs compared to nature reserves in the Czech Republic, but the number of endangered butterfly species was lower in MA than in nature reserves, with different species composition found in each region (Cizek et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, 82% of an additional 76 studied protected areas including NPs effectively maintained their species population due to low rates of habitat loss (Geldmann et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In fact, MAs have recently been recognized to act as wildlife refuges to a similar degree as NPs. One Brazilian study reported that endangered species that had disappeared in other areas were rediscovered in military zones (Arimoro et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, a MA in the Netherlands occupying only 1% of the land base contained 53% of the plant and 61% of the bird species found in the entire country (Lawrence et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNegative impacts on biodiversity conservation have also been documented in both areas. For example, the carnivorous black kite (\u003cem\u003eMilvus migrans\u003c/em\u003e) and red kite (\u003cem\u003eMilvus milvus\u003c/em\u003e) have been negatively affected by human activity in NPs due to their wide home ranges (Sergio et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Additionally, field signs of large mammals have been found to decrease when more people use NP trails (Zhou et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In MAs, weapon discharges affect wildlife activities and vegetation and road and building construction alters the land type. Furthermore, noise from military activities deters wildlife from human and wildlife trails (Marshall et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lindenmayer et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For example, the loss of grassland due to military operations has been shown to decrease the flight and hunting activities of the ferruginous hawk (\u003cem\u003eButeo regalis\u003c/em\u003e; Andersen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2021, MAs covered 51.5 km2 or 0.05% of the total area of South Korea, which has been under a truce since 1953 (Jung \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ministry of National Defense 2023). In total, 6,168 species and 102 endangered species (38% of the total endangered species in the country) inhabit the demilitarized zone, which is one of the largest MAs in South Korea (Kim \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In this study, we compare the effects of a NP and MA on the local biota of Gyeryongsan National Park, which includes Gyeryongsan Mountain. The headquarters of the South Korean Armed Forces (Gyeryong-dae) is a MA located on this mountain that has only minor disturbances related to civilian and military training access. Since these two areas are connected, they have similar fauna; however, they differ in their levels of anthropogenic disturbance due to their differing land-use types (i.e., NP or MA). This study compares the conservation of medium and large-sized mammals in the NP and MA of Gyeryongsan National Park to determine differences in the diversity and presence probabilities between the two areas and to determine how species spatiotemporally occupy the NP and MA.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study areas\u003c/h2\u003e \u003cp\u003eGyeryongsan Mountain (36\u0026deg;21'N, 127\u0026deg;13'E) has an altitude of 847 m and a mean slope of 15.6\u0026deg;. The mean annual temperature is 11\u0026deg;C, with a range of 16℃ to 25\u0026deg;C in summer and \u0026minus;\u0026thinsp;10℃ to \u0026minus;\u0026thinsp;1\u0026deg;C in winter. In total, 56% of the annual precipitation of 1,326.6 mm occurs between June and August. The Gyeryongsan Mountain national park (65.335 km2; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) includes the Gyeryong-dae MA (29.75 km2; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A portion of the NP is open to visitors and includes 52.15 km of trails and a total visitor area of 56.05 km: Access to the MA is restricted, but the NP is frequently visited due to the nearby metropolitan city of Daejeon (Korea National Park Service \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A total of 4,502 species, including four endangered species, inhabit this mountain. The main forest type is broad-leaved, but vegetation differs depending on altitude. In general, Korean red pine (\u003cem\u003ePinus densiflora\u003c/em\u003e) and Mongolian oak (\u003cem\u003eQuercus mongolica\u003c/em\u003e) are located above 650 m, Bakdal birch (\u003cem\u003eBetula schmidtii pegel\u003c/em\u003e) and palmate maple (\u003cem\u003eAcer palmatum\u003c/em\u003e) are found at 400\u0026ndash;650 m, and Korean red pine (\u003cem\u003ePinus densiflora\u003c/em\u003e) and elm-like trees (\u003cem\u003eZelkova serrata\u003c/em\u003e) are found at 200\u0026ndash;400 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Camera trapping\u003c/h2\u003e \u003cp\u003eCamera trapping was conducted from May to November 2021 at 21 sites (15 NP and 6 MA sites). Two types of cameras were used: Browning BTC-6PXD (Browning, USA); and Trophy Camera HD (Bushnel, USA). These were installed where wildlife field signs were found (e.g., footprints, scats, or rubbed trees) and were set up for taking three consecutive pictures at 30s intervals over the course of the observation period, for at least on month at each study site (Meek et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The coordinate information for the cameras was collected using a handheld global positioning system (Garmin 64S, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Environmental variables influencing medium and large-sized mammals\u003c/h2\u003e \u003cp\u003eSince each species can be affected by additional environmental factors other than the type of conservation area (NP or MA), 41 environmental variables reported to affect medium and large-sized mammals were collected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Land-use types (urban, agricultural, forest, grass, wetland, barren, and water areas) were identified based on a land-cover map produced by the Ministry of Environment (2021) and a trail map produced by the Korea Forest Service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.forest.go.kr\u003c/span\u003e\u003cspan address=\"https://www.forest.go.kr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To determine the most relevant spatial range that could explain the effects of the different variables on the studied species, the area of land use was identified at three different scales: 50, 500, and 1000 m using the buffer tool and calculate geometry dialog box in ArcMap v. 10.5 (Esri, USA; Hong and Joo \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Distances were measured using the near function of the same program. Landscape variables (e.g., aspect, slope, and altitude) were collected based on digital elevation models produced by the National Geographic Information Institute of Republic of Korea (NGII) (Hong et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the sites were classified as NPs or MAs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassifications and related references for the 41 selected variables. Anthropogenic and landscape factors were determined based on their distances to the camera trapping sites and areas at radii of 50, 500, and 1000 m\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthropogenic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrocardo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDhakal et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTian et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLezzi et al., 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLezzi et al., 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZhou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of conservation area (NP or MA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGray et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cizek et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTian et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTian et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrocardo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDhakal et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data analysis\u003c/h2\u003e \u003cp\u003eLinear models (LM) were used to determine the effects of the environmental variables on species diversity. The capture histories for each species at the different study sites were refined to measure species occurrence at the community level. Briefly, photos of species that occurred more than once within a 30 min interval were removed to reduce reporting bias of the same individuals (Farris et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The species occurrence frequencies and the Shannon diversity indexes (diversity) of the frequencies between species were calculated as follows:\u003c/p\u003e \u003cp\u003eOccurrence frequency of species =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{the number of occurrences}{camera activation period}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eDiversity (H) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-\\sum [{p}_{i}\\times \\text{l}\\text{n}\\left({p}_{i}\\right)]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e indicates the proportion of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e species.\u003c/p\u003e \u003cp\u003eMulti-model inferences were used to define the influential variable for diversity (Shannon diversity). The relative ranks of candidate models were determined based on the lowest Akaike's information criterion (AICc), which was corrected for small sample sizes (Anderson and Burnham \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Collinearity between the selected environmental variables was assessed based on the variance inflation factor (VIF) value, with high collinearity (VIF\u0026thinsp;\u0026ge;\u0026thinsp;5) variables excluded. The dredge function of the MuMIn package was then executed using all combinations of the environmental variables (Barton \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Models with AICc\u0026thinsp;\u0026lt;\u0026thinsp;2 were averaged to globally define the significant variables. The explanatory variables were selected by repeating the process after the variables from the averaged models that were not considered covariates were removed, since they did not show statistical significance (MacNally \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). If the weights of the multi-models did not show competency (weight\u0026thinsp;\u0026ge;\u0026thinsp;0.8), we considered the averaged model as the most parsimonious (Anderson and Burnham \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The accuracy of the best fit model was determined based on the R-squared coefficients between the observed and expected diversity of each species.\u003c/p\u003e \u003cp\u003eWe then performed non-metric multidimensional scaling (nMDS) with 41 environmental factors using the Bray-Curtis coefficient to ordinate the species occurrence frequency between the NP and MA and conducted a Monte Carlo randomization test. To evaluate the taxon similarity between the two regions, a pairwise analysis of similarities (ANOSIM) and a permutational multivariate analysis of variance (PERMANOVA) were used. We used a similarity percentage analysis (SIMPER) to determine which indicator species differentiated the two regions. All multivariate analyses and visualizations were performed using the ggplot2 package in R (version 3.5.0).\u003c/p\u003e \u003cp\u003eTo define the protective role of each area (NP and MA) at the species level, we applied the multispecies occupancy models in R to estimate the interactions between individual species and mammalian communities in terms of the environmental variables using the Bayesian approach (Mackenzie et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The significant variables were used as fixed effects based on previous analyses (LM and nMDS) and species as random effects to compare the impacts of the NP and MA in association with the environmental variables. Additionally, a fixed effect was applied to indicate whether the site was located in the NP or the MA to test the second hypothesis. Multispecies occupancy modeling was performed using the community model function in the camtrap package in R (Hubbard et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The 10 day presence and absence values were divided into 1 bin and the number of iterations, burn-ins, and chains was 1000, 500, and 3, respectively.\u003c/p\u003e \u003cp\u003eWe compared the occurrence frequencies between the NP and MA to examine the differences in the temporal activity pattern of each species. Camera trap data, including time of capture, was used to create a density plot to depict the distribution of photos taken over 24 hours. The type of conservation area was added as a column of data. Diel activity was measured by comparing and overlapping the activity patterns from all medium and large-sized mammal species that occurred in the NP and MA using kernel density plots. The overlap coefficient was calculated as the proportion of overlap between the two diel activity curves in the NP and MA using the overlap package in R (Meredith et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The overlap was calculated with 999 bootstraps to obtain a 95% confidence interval. The values ranged from 0 (no overlap) to 1 (complete overlap).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Medium and large-sized mammals inhabiting the NP and MA\u003c/h2\u003e \u003cp\u003eIn total, six families and eight species of medium and large-sized mammals concurrently inhabited the NP and MA. Two endangered species, the leopard cat (\u003cem\u003ePrionailurus bengalensis\u003c/em\u003e) and yellow-throated marten (\u003cem\u003eMartes flavigula\u003c/em\u003e), were found in both areas. The species were classified as follows: 3 carnivores (Siberian weasel (\u003cem\u003eMustela sibirica\u003c/em\u003e), leopard cat (\u003cem\u003eP. bengalensis\u003c/em\u003e), and yellow-throated marten (\u003cem\u003eM. flavigula\u003c/em\u003e)); 3 omnivores (wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e), raccoon dog (\u003cem\u003eNyctereutes procyonoides\u003c/em\u003e), and badger (\u003cem\u003eMeles leucrus\u003c/em\u003e)); and two herbivores (water deer (\u003cem\u003eHydropotes inermis\u003c/em\u003e) and Korean hare (\u003cem\u003eLepus coreanus\u003c/em\u003e)). The mean diversity between the NP (1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) and MA (1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) was similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the study sites, the F10 site in the NP had the greatest diversity, whereas the C61 site in the NP showed the least diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Three sites (F8 and F10 in the NP, and the HBC site in the MA) had the highest number of species (7 species) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The E1 site in the MA showed the lowest number of species (2 species).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Differences between the NP and MA at the community level\u003c/h2\u003e \u003cp\u003eAmong the 1,023 models evaluated, the best fit model was based on a linear model with slope used as the significant predictor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A high slope (coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06) indicated low mammal community diversity (r2\u0026thinsp;=\u0026thinsp;0.23, adjusted r2\u0026thinsp;=\u0026thinsp;0.19). However, type of conservation area (NP of MA), as a factor that could compare for degree of conservation between two regions, was not selected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe medium and large-sized mammal communities divided between the NP and MA were clustered; thus, no significant differences were noted in the nMDS analysis. The variable loading results showed that the slope (r2\u0026thinsp;=\u0026thinsp;0.25; p\u0026thinsp;=\u0026thinsp;0.09) and distance from the trail (TRAIL_DIST; r2\u0026thinsp;=\u0026thinsp;0.28; p\u0026thinsp;=\u0026thinsp;0.05) contributed significantly to the ordination axis 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two factors explained that the mammalian communities depended more on the geographical or anthropogenic components of their habitats than on differences in the type of conservation area (NP or MA). The ANOSIM results supported the position of the slope and distance from the trail as determined by ordination. There was a greater dissimilarity between the two factors than within them (R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.09; p\u0026thinsp;=\u0026thinsp;0.72). Badgers (\u003cem\u003eM. leucrus\u003c/em\u003e), raccoon dogs (\u003cem\u003eN. procyonoides\u003c/em\u003e), and yellow-throated martens (\u003cem\u003eM. flavigula\u003c/em\u003e) appeared more frequently at greater distances from trails with lower slopes, which contrasted with the water deer patterns (\u003cem\u003eH. inermis\u003c/em\u003e). The results of PERMANOVA supported differences in the mammalian community composition between the NP and MA (pseudo-F\u0026thinsp;=\u0026thinsp;0.80; p\u0026thinsp;=\u0026thinsp;0.589), but these were not significant. The slope and distance from the trail separated the communities. The SIMPER analysis showed that raccoon dogs (\u003cem\u003eN. procyonoides\u003c/em\u003e; 19.3%) were a significant indicator species differentiating the two regions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimilarity percentage (SIMPER) analysis results of the species compositions for the mammal communities in the national park (NP) and military area (MA). The four species with the highest contributions are provided\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency in the NP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency in the MA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContribution (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater deer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBadger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild boar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaccoon dog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Differences in conservation effect between the NP and MA by species\u003c/h2\u003e \u003cp\u003eThe preference of each species for the slope or distance from the trail, which were selected as significant predictors, differed between the LM and nMDS analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). The slope results revealed that three of the eight species (i.e., the yellow-throated marten (\u003cem\u003eM. flavigula\u003c/em\u003e), Korean hare (\u003cem\u003eL. coreanus\u003c/em\u003e), and leopard cat (\u003cem\u003eP. bengalensis\u003c/em\u003e)) had values on the left in the middle, indicating that they preferred flat areas. Regarding the distance from the trail, the yellow-throated marten (\u003cem\u003eM. flavigula\u003c/em\u003e) was the only species with values on the right in the middle; thus, yellow-throated martens (\u003cem\u003eM. flavigula\u003c/em\u003e) preferred to live far from the trail.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe preference of each species for inhabiting the NP or the MA differed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Of the eight species, only the Korean hare (\u003cem\u003eL. coreanus\u003c/em\u003e) showed values on the right in the middle, indicating a preference for the NP. Five species preferred the MA (raccoon dogs (\u003cem\u003eN. procyonoides\u003c/em\u003e), wild boars (\u003cem\u003eS. scrofa\u003c/em\u003e), leopard cats (\u003cem\u003eP. bengalensis\u003c/em\u003e), badgers (\u003cem\u003eM. leucrus\u003c/em\u003e), and water deer (\u003cem\u003eH. inermis\u003c/em\u003e)). Siberian weasels (\u003cem\u003eM. sibirica\u003c/em\u003e) and yellow-throated martens (\u003cem\u003eM. flavigula\u003c/em\u003e) inhabited the two regions similarly.\u003c/p\u003e \u003cp\u003eActivity time was similar in both regions, but differed for three of the carnivore species. In particular, the activity diel of the yellow-throated marten (\u003cem\u003eM. flavigula\u003c/em\u003e; overlap ratio\u0026thinsp;=\u0026thinsp;0.6; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh), Siberian weasel (\u003cem\u003eM. sibirica\u003c/em\u003e; overlap ratio\u0026thinsp;=\u0026thinsp;0.3; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee) and leopard cat (\u003cem\u003eP. bengalensis\u003c/em\u003e; overlap ratio\u0026thinsp;=\u0026thinsp;0.38; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) changed depending on region (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Siberian weasel (\u003cem\u003eM. sibirica\u003c/em\u003e) was nocturnal in the MA, but was active during both the day and night in the NP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). The leopard cat (\u003cem\u003eP. bengalensis\u003c/em\u003e), was active during the day and night in MA, but nocturnal NP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Four species (badger, (\u003cem\u003eM. leucrus\u003c/em\u003e), raccoon dog, (\u003cem\u003eN. procyonoides\u003c/em\u003e), water deer, (\u003cem\u003eH. inermis\u003c/em\u003e) and wild boar (\u003cem\u003eS. scrofa\u003c/em\u003e)) were documented a similar number of times (overlap ratio of badger\u0026thinsp;=\u0026thinsp;0.86; raccoon dog\u0026thinsp;=\u0026thinsp;0.88; water deer\u0026thinsp;=\u0026thinsp;0.79; wild boar\u0026thinsp;=\u0026thinsp;0.82) in both areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides a comprehensive comparison of the conservation roles of a NP and MA for medium and large-sized mammals. The two connected regions had the same temperate mountain climate and similar forest types. Similar forest communities generally show comparable diversities in forest mammal species, which was also found in this study (Iezzi et al. 2021). Despite their connectivity, most parts of the studied NP and MA were distant from each other. Distribution differences in mammals sensitive to habitat heterogeneity are caused by differences in overall habitat components, which complicates the determination of habitat preference for wildlife in those regions. For this reason, we distinguished the habitat preferences for medium and large-sized mammals. The two regions selected for this study provide a good comparison, since they are located on the same mountain with similar habitats and environments.\u003c/p\u003e \u003cp\u003eThe LM and nMDS analyses identified that the importance of slope and distance from trail differed depending on the diversity and composition of the mammalian community. Wildlife generally chooses sloped gradients for rearing and hiding infants to utilize the features of inclined planes (Jeong et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, mammals used flat topographies to move to other habitats. Most wildlife use trails for various purposes, including foraging, movement, and communication with other animals (Hill et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Herbivorous and omnivorous mammals were less prevalent in the geographical areas of this study; the trail located on a low-slope road in the forest that was frequently accessed by predators and humans had a low abundance of certain herbivores and omnivores. Thus, to maintain a diverse medium and large-sized mammal community, the slope and distance from the trail must be considered.\u003c/p\u003e \u003cp\u003eThe multispecies occupancy model also suggested that the studied mammalian species preferred different environments depending on the slope and distance from the trail. These geographical factors often provide marking areas for predators. For example, carnivores deposit their scats on a cross road in a forest to improve the likelihood of detection from other animals (Barja et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Similarly, our results indicate that the yellow-throated marten (\u003cem\u003eM. flavigula\u003c/em\u003e) and leopard cat (\u003cem\u003eP. bengalensis\u003c/em\u003e) may use ridges as convenient routes through the mountain for movement; however, mammals prefer to move through gentle-slope areas to other habitats when these are not accessed by humans (Tobler et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In particular, carnivores, such as the red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e) and ocelot (\u003cem\u003eLeopardus pardalis\u003c/em\u003e), prefer flat areas but are not found when tourists and hikers use the trail (Harmsen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Suzuki et al. 2023). Of the species that preferred a gentle-slope area, yellow-throated martens (\u003cem\u003eM. flavigula\u003c/em\u003e) avoided trail hikers because they were sensitive to human disturbance. Additionally, some herbivores prefer flat habitats due to food availability. For example, Korean hares (\u003cem\u003eL. coreanus\u003c/em\u003e) were primarily found in grasslands with lower slopes where they could readily find grasses and tree stems as food resources. Generally, most mammalian species used gentle slopes and trails for multiple purposes, but often avoided the areas used by humans.\u003c/p\u003e \u003cp\u003eThe multispecies occupancy model showed that the preference for living in the NP or MA differed among the studied species. The Korean hare (\u003cem\u003eL. coreanus\u003c/em\u003e) only inhabited the NP, but most of the species preferred the MA environment. The distribution of medium and large-sized mammals that are sensitive to human activity decreases as human presence increases. In the Hengduan Mountains of China, the proportion of herbivores significantly decreases as human presence increases and carnivore presence also significantly declines with increased human disturbance (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To avoid encountering people, mammals use the MA for refuge. When herbivores occur frequently in a region, the frequency of predation increases (Owen-Smith \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To avoid predators, the Korean hare (\u003cem\u003eL. coreanus\u003c/em\u003e) might select the most effective area because this species has a smaller home range than other mammalian herbivores. Thus, to activate their natural ranging behavior while avoiding the risk of natural enemies, such as predators and humans, such species could be different their preference between the NP and MA as needed.\u003c/p\u003e \u003cp\u003eAlthough the majority of the temporal activity patterns were similar for the studied species, they differed for carnivores depending on human activity in both areas. The NP has a greater human presence (e.g., tourists and hikers) that is usually active in the morning and before sunset. Some soldiers are also present in the MA around the clock, but their activities occur on a more regular schedule than those of the NP visitors. These human activities affect the timing and areas of carnivore activities. On the Iberian Peninsula, red fox activities (\u003cem\u003eVulpes vulpes\u003c/em\u003e) increased in areas far from human residences (Diaz-Ruiz 2016). Similarly, the yellow-throated marten (\u003cem\u003eM. flavigula\u003c/em\u003e) may move between the NP and MA to avoid humans and maintain their wide home range. For example, their activity density was higher and occurred throughout the day in the MA where human activity was less. The Siberian weasel (\u003cem\u003eM. sibirica\u003c/em\u003e) and leopard cat (\u003cem\u003eP. bengalensis\u003c/em\u003e) also displayed flexible behavior patterns between the two study regions, which further supports the conclusion that carnivores are more likely affected by human disturbance.\u003c/p\u003e \u003cp\u003eTime use for the medium and large-sized mammals tended to significantly differ due to differences in the degree of anthropogenic disturbance between the two regions. This might relate to a decline in productivity, behavior, abundance and richness of individuals, and community interactions. This tended to be especially true for carnivores, who were more affected by anthropogenic disturbance than the other mammals. Similarly, wolf movements occurred at different times than human activities in regions where human movement was high (Mori, 2020). According to our results, most carnivores preferred the flat areas, such as human trails. The timing of mammal activities was not always affected by human activities, as indicated by the result that most of the studied mammal species preferred the MA. Furthermore, some mammals changed their active periods and space use depending on human activities, which represents the importance of spatiotemporal separation in the NP.\u003c/p\u003e \u003cp\u003eDiversity could be increased in the NP by separating the accessibly and inaccessible sections of the park and by using time (i.e., active preiods) in a combined spatiotemporal strategy. The NP was selected by the government to serve as a protected ecosystem with various high-quality services provided to the public, such as ecotourism and animal and ecology education. These activities cause alterations in the timing of animal movements and space usage of mammals according to the proximity of the human activities to mammals. Recently, programs have been implemented to reduce animal stress from anthropogenic sources, such as the restriction of access time and division of available space. Since NP are used for both tourism and ecosystem conservation with the goal of the coexistence of several species, general species habitats and Red List species should be managed by separating the core conservation zones from the MA. Thus, to increase species diversity in NPs, we emphasize the importance of management strategies that separate species habitats into human accessible and restricted zones and times, including the restriction of hiking trail portions.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAlthough the characteristics of the studied NP and MA were similar in terms of forest ecosystem conservation, species diversity and the number of species that prefer the habitat were greater in the MA than in the NP. Despite the NP designation as a protected area, high human activity levels cause mammals to avoid certain areas either completely or at times of high human activities, which has a negative effect on conservation. Thus, protected areas require more suitable management to increase diversity, such as through spatial and temporal separation of accessible and inaccessible sections.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all researchers who helped with the surveys with field assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors disclose that no use of generative AI and AI-assisted technologies have been used 396 in the writing process\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Sejong Science Fellowship, the National Research Foundation of Korea (NRF) [grant number NRF-2021R1C1C2004162].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData curation, investigation, methodology; writing-original draft were performed by [Mihyeon Kim]. The conceptualization, methodology, formal analysis, validation, visualization were performed by [Hyo Gyeom Kim]. The conceptualization, writing \u0026ndash; review \u0026amp; editing, supervision, project administration, funding acquisition were performed by [Sungwon Hong].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson D, Burnham K (2004) Model selection and multi-model inference. Second ed. Springer-Verlag, New York\u003c/li\u003e\n\u003cli\u003eAndersen MC, Thompson B, Boykin K (2004) Spatial risk assessment across large landscapes with varied land use: lessons from a conservation assessment of military lands. 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Although both regions have positive and negative effects on animal communities, studies comparing their impacts on conservation are limited. We monitored mammalian communities using 21 sensor cameras in a MA (6 sites) and NP (15 sites) located on Gyeryongsan Mountain, South Korea from May to November 2021 to compare the conservation of medium and large-sized mammals between the two regions. Forty-one environmental variables, including anthropogenic and geographical factors, were extracted from different spatial ranges (50, 500, and 1000 m). A linear model and non-metric multidimensional scaling were used to identify the factors influencing community diversity. We also analyzed species habitat type preferences using a multispecies occupancy model and compared temporal activities in the two regions. Species diversity was similar between the two sites, with most animals preferring habitats with lower slopes located at greater distances from human trails. Only the Korean hare (\u003cem\u003eLepus coreanus\u003c/em\u003e) preferred the NP habitat of the eight species found. Active periods were similar for the species in both regions, except for differences in some carnivore species. Although not all species were affected by human activity, most preferred the MA over the NP. The carnivores were especially able to flexibly alter their active periods and locations in response to human activities, especially in the NP where human activity was more prevalent. Thus, strategies should be implemented to improve NP conservation success, such as spatial and temporal accessible and inaccessible section separation.\u003c/p\u003e","manuscriptTitle":"Comparison of the conservation of medium and large-sized mammals in a national park and military area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-30 17:10:02","doi":"10.21203/rs.3.rs-3667778/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":"bce00a33-4250-463f-9fd6-9dae320e20e1","owner":[],"postedDate":"November 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-07T19:29:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-30 17:10:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3667778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3667778","identity":"rs-3667778","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}