Understanding 20 years of vegetation change in grasslands by focusing on changes in the cover of Sasa hayatae and Miscanthus sinensis

Understanding 20 years of vegetation change in grasslands by focusing on changes in the cover of Sasa hayatae and Miscanthus sinensis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 January 2025 V1 Latest version Share on Understanding 20 years of vegetation change in grasslands by focusing on changes in the cover of Sasa hayatae and Miscanthus sinensis Authors : Hideyuki Niwa 0000-0002-8199-3248 [email protected] , Guihang Dai , Midori Ogawa , and Mahito Kamada Authors Info & Affiliations https://doi.org/10.22541/au.173707563.38061276/v1 245 views 103 downloads Contents Abstract Introduction Methods Results Discussion Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The impact of Cervus nippon browsing on vegetation in grasslands in Japan has become pronounced. In obtaining useful information for the management of grasslands affected by C. nippon browsing, we aimed to evaluate vegetation changes caused by browsing on multiple axes based on vegetation survey data from two time periods and to use UAV-mounted LiDAR data to determine the distribution of indicator species of vegetation change on an areal scale. The study area was approximately 23 ha around the Ochiai Pass in Higashi Iya Ochiai, Miyoshi City, Tokushima Prefecture. A vegetation survey was conducted in 2022 at the same 35 sites as in 2002 to understand the changes in vegetation. The ordination using nonmetric multidimensional scaling (NMDS) revealed that the changes in the cover of Sasa hayata e and Miscanthus sinensis and in the current cover of M. sinensi s caused by the changes affected the vegetation in the study area. NMDS revealed that the entire study area is not changing along a single axis. The data acquired by the UAV-mounted LiDAR were used to estimate the density of S. hayatae and M. sinensis , which are indicators of vegetation change, on an areal basis. A high correlation was determined between the mean values of the reflected intensity at heights of 0.7–0.8 m for S. hayatae and 1.4–1.5 m for M. sinensis . The methods used in this study were useful for monitoring spatial and temporal changes in vegetation, and they could be applied to the management of different types of grasslands. Understanding 20 years of vegetation change in grasslands by focusing on changes in the cover of Sasa hayatae and Miscanthus sinensis Hideyuki Niwa 1 , Guihang Dai 2 , Midori Ogawa 2 , Mahito Kamada 3 1 Faculty of Bioenvironmental Science, Kyoto University of Advanced Science 2 Graduate School of Advanced Technology and Science, Tokushima University 3 Graduate School of Technology, Industrial and Social Sciences, Tokushima University #Corresponding Author Hideyuki Niwa, Dr. Global Environmental Studies. Faculty of Bioenvironmental Science, Kyoto University of Advanced Science 1-1 Sogabe-cho Nanjyo Otani, Kameoka City, Kyoto 621-8555 Japan 08-0771-29-3518 [email protected] ORCID 0000-0002-8199-3248 Abstract The impact of Cervus nippon browsing on vegetation in grasslands in Japan has become pronounced. In obtaining useful information for the management of grasslands affected by C. nippon browsing, we aimed to evaluate vegetation changes caused by browsing on multiple axes based on vegetation survey data from two time periods and to use UAV-mounted LiDAR data to determine the distribution of indicator species of vegetation change on an areal scale. The study area was approximately 23 ha around the Ochiai Pass in Higashi Iya Ochiai, Miyoshi City, Tokushima Prefecture. A vegetation survey was conducted in 2022 at the same 35 sites as in 2002 to understand the changes in vegetation. The ordination using nonmetric multidimensional scaling (NMDS) revealed that the changes in the cover of Sasa hayatae and Miscanthus sinensis and in the current cover of M. sinensis caused by the changes affected the vegetation in the study area. NMDS revealed that the entire study area is not changing along a single axis. The data acquired by the UAV-mounted LiDAR were used to estimate the density of S. hayatae and M. sinensis , which are indicators of vegetation change, on an areal basis. A high correlation was determined between the mean values of the reflected intensity at heights of 0.7–0.8 m for S. hayatae and 1.4–1.5 m for M. sinensis . The methods used in this study were useful for monitoring spatial and temporal changes in vegetation, and they could be applied to the management of different types of grasslands. Keywords Sasa hayatae , Miscanthus sinensis , grassland, Cervus nippon , browsing, UAV, LiDAR, NMDS Introduction In North America, Europe, Japan, and New Zealand, high-density deer have an altered vegetation structure and species composition (Gill 1992). In Japan, the range of Cervus nippon continues to expand (Takahashi et al. 2013). Deer browsing reduces the number of highly palatable plants (Anderson & Katz 1993; Horsley et al. 2003; Rooney 2009; Inatomi et al. 2017; Otsu et al. 2019), allowing unpalatable and browse-resistant plants to dominate (Anderson & Katz 1993; Horsley et al. 2003; Rooney & Waller 2003; Rooney2009). In general, ferns (Rooney & Dress 1997) and browse-tolerant gramineous and sedge plants (Rooney 2009), which are often unpalatable species, will become dominant. The continued dominance of unpalatable or browse-tolerant plants may not restore vegetation to its original state even after deer control (Tanentzap et al. 2009; Nuttle et al. 2014), and vegetation does not recover to its original state more than 20 years after deer browsing has been reduced (Nuttle et al. 2014; Boulanger et al. 2015). In Japan, grassland biomes are not found except for grasslands in high mountains and coastal areas. Grasslands in mountainous and lowland areas are maintained by human disturbances such as mowing, grazing, and burning (Yamamoto et al. 1997). Even in these grasslands, the impact of deer has become more pronounced, and the disappearance of Sasa hayatae and the dominance of Miscanthus sinensis caused by the browsing of C. nippon have been reported (Takatsuki et al. 2021). Some species are unique to grasslands, and changes in vegetation caused by the browsing of C. nippon have made it a challenge to conserve these species (Okubo 2002). Plant communities are spatially diverse, and responses to a given browse pressure likely vary within and among plant communities (Weisberg et al. 2005; Wisdom et al. 2006). In general, grassland plant communities are in the process of secondary succession, and community dynamics are diverse depending on the type, intensity, frequency, and duration of disturbance (Okubo 2002). Therefore, understanding them with regard to a single axis of succession, such as progression or regression, is difficult (Yamamoto 2001). In grassland management, understanding the spatial and temporal changes in vegetation with high spatial resolution is important (Baeza et al. 2010; Zhang et al. 2021), and remote sensing is an effective tool for this purpose (Maake et al. 2023). Remote sensing of grasslands has primarily used optical imagery, such as near-infrared (Maake et al. 2023). LiDAR, which has recently emerged, can be used to measure information about vegetation that cannot be measured optically with high spatial resolution (Zhang et al. 2021). Although some studies have used LiDAR data to classify grassland types (Fisher et al. 2018), monitoring grasslands using LiDAR data has not been well studied (Zhang et al. 2021). Furthermore, the latest technology, UAV-mounted LiDAR, can remarkably reduce the cost of data acquisition, which is a drawback of conventional aircraft-mounted LiDAR (da Rocha et al. 2023). Therefore, the development of methods for monitoring grasslands using UAV-mounted LiDAR is an important research topic. This study aimed to obtain useful information for the management of grasslands affected by C. nippon browsing by (1) evaluating vegetation changes caused by C. nippon browsing on multiple axes based on vegetation survey data from two time periods and (2) using UAV-mounted LiDAR data to determine the distribution of species that indicate vegetation changes on an areal basis. Methods Study Site The study site was a 23-ha area around the Ochiai Pass in Higashi Iya Ochiai, Miyoshi City, Tokushima Prefecture (Fig. 1). The study site is located in the Kenzan mountain range in the eastern part of the Shikoku Mountains at an elevation of 1,560 m above sea level. The climatic zone of the study site is cool temperate. The study area was a grassland dominated by M. sinensis until around 1990, but around 2005, it became a grassland dominated by S. hayatae (Kogushi et al. 2005). At present, S. hayatae is distributed mainly along the ridge, interspersed with trees such as Abies homolepis . The establishment of grasslands in the study area is due to the influence of previous human disturbances, such as fire burning (Kamada 1994). No human disturbance is currently occurring in the study area. In Japan, the impact of browsing damage on vegetation has become more pronounced because of the increase in deer population density. According to a survey conducted by the Shikoku Regional Forest Office in 2011, the density of C. nippon in the vicinity of the study area was remarkably high at 49.5 deer/km 2 , which has had a great impact on vegetation. In the study area, the effects of C. nippon browsing were also observed, including the browsing scars of S. hayatae and the death of shrubs caused by browsing damage. In addition, A. homolepis has died because of bark stripping by C. nippon (Niwa et al. 2023). Vegetation Survey A vegetation survey was conducted in 2022 at the same 35 sites as in 2002 to understand the changes in vegetation. The 2022 survey was conducted at approximately the same locations as in 2002 by importing the locations of the 2002 survey sites into a single positioning GNSS (Garmin GPSMAP 66i) and confirming them in the field. The survey method was the same for both years. A 2 m × 2 m quadrat for the herbaceous community and a 5 m × 5 m quadrat for the woody community were set up. The layers were divided into the first subtree layer (T1), second subtree layer (T2), shrub layer (S), and herbaceous layer, and the species occurrence and percent cover were recorded for each layer. The 2002 survey was conducted from September 6 to September 8, and the 2022 survey was conducted in October 11 and October 12. The vegetation survey results were used for ordination to evaluate vegetation change on multiple axes. The percent cover of each layer in 2022 was used as the species data. In addition, the percent cover of S. hayatae in 2002 and 2022, the 2-year change in percent cover, the percent cover of M. sinensis in 2002 and 2022, and the 2-year change in percent cover were used as environmental factor data. The data were ordinalized using nonmetric multidimensional scaling (NMDS). Estimation of the Density of and The data acquired using the UAV-mounted LiDAR were used to estimate the density of S. hayatae and M. sinensis as indicators of vegetation change in an areal basis. DJI L1 was used on a DJI Matrice 300. In this case, the horizontal field of view was 70.4°; the vertical field of view was 4.5°; the scan rate was 480,000 points/s, and the system accuracy was 10 cm horizontal and 5 cm vertical. Using UgCS (SPH Engineering, Latvia, EU), a flight route was created with a flight altitude of 60 m and a sidelap of 70%. The created flight route was imported into the DJI Pilot2 and measured at a flight speed of 5 m/s. Simultaneously, A DJI D-RTK 2 mobile station was installed, and the position was corrected by real-time kinematics. October 12, 2022 data were measured. The measured data were postprocessed in DJI Terra to obtain a 3D point cloud model. After removing outliers from the 3D point cloud model, the ground surface point cloud was classified using default parameters. A 5 cm × 5 cm digital terrain model (DTM) was created by interpolating the point cloud of the ground surface using the triangulated irregular network method. DTM was used to correct the height of the 3D point cloud model from elevation to ground level (normalization). The normalized 3D point cloud model was extracted at 10-cm intervals up to 2 m above ground level, and a 10 m × 10 m grid was used as the aggregation unit to calculate the number of point clouds and the mean reflection intensity for the first return at each 10-cm interval. Correlations between S. hayatae cover in 2022 and M. sinensis cover in 2022 at the study sites and the grid aggregate values were analyzed for each 10-cm interval. Results Vegetation Survey Sixty-three species in 40 families were identified during the 2022 survey (Table 1). One species was on the Red List of the Ministry of the Environment; four species were on the Red List of Tokushima Prefecture, and 15 species were unpalatable species of C. nippon (Hashimoto & Fujiki2014) The NMDS stress value was 1.7, which is a medium fit. Different environmental factors played a role in the M. sinensis cover 2022, change in S. hayatae cover, and change in M. sinensis cover (Table 2). The distribution of the survey sites and vectors of environmental factors is shown in Fig. 2. The survey sites were classified into nine classes based on their spatial grouping in the coordinate space, and their distribution is shown in Fig. 3. The distribution of species and vectors of environmental factors is shown in Fig. 4. More species were distributed in areas with high M. sinensis cover than in areas with high S. hayatae cover. The endangered species were distributed in areas with increasing M. sinensis cover and areas with little change in S. hayatae cover and M. sinensis cover. Furthermore, unpalatable species of C. nippon were randomly distributed. Estimation of the Density of and The results of the 2022 vegetation survey showed that the mean and maximum vegetation heights of S. hayatae were 53 and 160 cm, respectively, and those of M. sinensis were 84 and 200 cm, respectively (Fig. 5). The correlations between the S. hayatae cover in 2022 and M. sinensis cover in 2022 at the study sites and the grid tabulations were analyzed for each 10-cm interval (Table 3), with many R2 values <0.01, all of which were low. In addition, a high correlation was determined between the mean values of the reflected intensity at heights of 0.7–0.8 m in S. hayatae and 1.4–1.5 m in M. sinensis , where R2 was the largest and the P -value was the smallest. The mean values of the reflected intensity at those heights and the S. hayatae cover in 2022 and M. sinensis cover in 2022 at the study sites are shown in Figs. 6 and 7. Discussion Vegetation Change The study area had been a grassland dominated by M. sinensis until about 1990, but around 2005, it became a grassland dominated by S. hayatae (Kogushi et al. 2005). In Japan, where the climax is forest, M. sinensis grasslands often persist because of human disturbances such as mowing, burning, and grazing (Sakagami 2001; Yamamoto et al. 2007). At the study site, M. sinensis grasslands were also established by human disturbances such as burning (Kamada 1994). Sasa species can grow vigorously and overwhelm M. sinensis even under M. sinensis dominance by receiving nutrients from the parent plant through underground stems (Hashimoto et al. 2012). Therefore, the study site likely became a grassland dominated by S. hayatae around 2005, after human disturbance had ceased. Sasa spp. compete with tree seedlings for light availability, and their high cover prevents tree restoration (Nakashizuka 1988). Therefore, the vegetation in the study area can cause S. hayatae grassland to intersperse with trees such as A. homolepis . Sasa spp. are evergreen, with high above-ground biomass, and they are highly valuable as food for deer (Yokoyama et al. 2000; Takatsuki et al. 2021). Sasa spp. also serve as as winter food (Tanaka et al. 2008; Takahashi et al. 2013). Grasslands dominated by Sasa spp. revert to M. sinensis grasslands upon resumption of human disturbances (Sakagami et al. 1995; Yamamoto et al. 2007; Hashimoto et al. 2012; Takatsuki & Uehara 2021). At the study site, the population density of C. nippon was high around 2011, and C. nippon browsing is considered to be a disturbance, causing a part of the grassland dominated by S. hayatae to return to the grassland dominated by M. sinensis . The identification of 15 (24%) unpalatable species of C. nippon in the 2022 vegetation survey indicates that the area has been affected by C. nippon browsing. In the ordination using NMDS, environmental factors were considered significant for M. sinensis cover 2022, change in S. hayatae cover, and change in M. sinensis cover. The change in S. hayatae cover and M. sinensis cover as well as the M. sinensis cover 2022 caused by the changes affected the vegetation in the study area. The vectors of the change in S. hayatae cover and M. sinensis cover were in opposite directions, and the study sites were distributed in both directions. As mentioned earlier, the NMDS revealed that the entire study site was not changing along a single axis, although C. nippon browsing likely returns the S. hayatae -dominated grassland to M. sinensis -dominated grassland at the study site. Class 1 areas tend to be dominated by herbaceous plants with above-ground parts near the ground surface. C. nippon forages on M. sinensis but only if they pinch the tips of M. sinensis leaves. Moreover, the young leaf stage is often foraged by C. nippon (Takatsuki & Uehara 2021). Unpalatable plants and browse-resistant species cover nearby edible species and protect them from browsing (Milchunas & Noy-Meir;2002; Callaway et al. 2005; Takatsuki & Uehara 2021). In addition, the change from grassland to M. sinensis grassland improves near-surface light conditions and increases the number of species and populations of grassland plants (Hashimoto et al. 2012). Thus, Class 1 is considered to be a place where M. sinensis cover increases because of C. nippon browsing and where grassland vegetation can grow because of improved light conditions and protection from browsing. Class 1 is important for the conservation of grassland plant diversity in the study area because it contains the endangered species Geranium shikokianum var. shikokianum . Classes 2, 3, and 6 are the areas where a slight change in S. hayatae cover and M. sinensis cover is observed. Classes 2 and 3 are considered to be the areas where S. hayatae continues to dominate, in which the high cover of S. hayatae has resulted in few distributed species, and where only shrubs such as the pioneer species Fallopia japonica and the unpalatable species Rosa multiflora can grow. Class 6 is the area where M. sinensis continues to dominate. Apart from grassland plants, many species that are naturally distributed in the study site are found in the area, such as the endangered species Tricyrtis macropoda and Rhododendron tsurugisanense , which are important for the conservation of plant diversity in the study area. Classes 4 and 5 are the areas where S. hayatae cover increased. Considering that only unpalatable and prickly plants are distributed in these areas, such areas are considered to be heavily influenced by C. nippon browsing. Class 4 is considered to be an over-humid area because of the distribution of Juncus decipiens . Classes 7, 8, and 9 are considered as the areas in which the influence of forests is significant because of the distribution of woody plants. Class 7 is an area of increased M. sinensis cover, and Class 9 is an area of increased S. hayatae cover. Class 8, located in the middle of Classes 7 and 9, is important for the conservation of plant diversity in the study area because of the distribution of the endangered species Oxalis nipponica subsp. nipponica . Estimation of the Density of and The highest correlations of mean reflectance intensity were determined at heights of 0.7–0.8 m for S. hayatae and 1.4–1.5 m for M. sinensis , where R2 was the largest and P -value was the smallest. Considering that the height selected was between the third quartile of vegetation height and the maximum value for S. hayatae and M. sinensis (Fig. 5), the point cloud data at the height where the above-ground parts of S. hayatae and M. sinensis were located were selected, which is considered a reasonable result. However, M. sinensis had maximum R2 at heights of 1.6–1.7 m and 1.7–1.8 m, with slightly higher P values. Therefore, M. sinensis have a high potential for density estimation in the 1.4–1.8 m height range. R2 was <0.01 in most cases, all of which were low. When measured at an altitude of 100 m, the size of the DJI L1 beam at the ground surface was relatively large, measuring 50 cm × 5 cm. Therefore, in dense vegetation such as S. hayatae and M. sinensis , a single return may have captured the surface of multiple plants, which may have contributed to low R2. However, by using only the first return, the point cloud was limited to only the surface of the vegetation, and by using the average reflection intensity to reflect the density of the vegetation, the height at which the density of S. hayatae and M. sinensis could be best estimated was selected. In the future, comparative validation using different LiDAR systems can estimate density with a higher correlation. Grassland Management In grassland management, understanding spatial and temporal changes in vegetation is important (Baeza et al. 2010; Zhang et al. 2021). The use of NMDS to capture 20 years of vegetation change in multiple axes allowed us to understand the spatially diverse impacts of C. nippon browsing and the importance of conserving plant species diversity. We were also able to estimate the density of S. hayatae and M. sinensis , which are indicators of vegetation change, using UAV-mounted LiDAR data. The combination of small-scale field sampling and remote sensing is an excellent approach to obtain important information for grassland management (Aragón & Oesterheld2008), which we demonstrated in this study. Furthermore, we could propose a method for monitoring grasslands using UAV-mounted LiDAR, which has rarely been studied. The method proposed in this study can be used to monitor spatial and temporal changes in vegetation and could be applied to the management of different types of grasslands. Declarations Authors’ contributions Hideyuki Niwa and Mahito Kamada conceived the ideas and designed methodology; Hideyuki Niwa and Guihang Dai and Midori Ogawa and Mahito Kamada collected the data; Hideyuki Niwa led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Data availability Data cannot be shared openly but are available on request from authors. Funding No funding was received for conducting this study. Conflicts of interest The authors have no relevant financial or non-financial interests to disclose. References Anderson, R.C., & Katz, A.J. 1993. Recovery of browse-sensitive tree species following release from white-tailed deer Odocoileus virginianus zimmerman browsing pressure. Biological Conservation 63: 203–208. Aragón, R., & Oesterheld, M. 2008. Linking vegetation heterogeneity and functional attributes of temperate grasslands through remote sensing. Applied Vegetation Science 11: 117–130. Baeza, S., Lezama, F., Piñeiro, G., Altesor, A., & Paruelo, J.M. 2010. Spatial variability of above-ground net primary production in Uruguayan grasslands: A remote sensing approach. Applied Vegetation Science 13: 72–85. Boulanger, V., Baltzinger, C., Said, S., Ballon, P., Picard, J.F., & Dupouey, J.L. 2015. Decreasing deer browsing pressure influenced understory vegetation dynamics over 30 years. ANNALS OF FOREST SCIENCE 72:. Callaway, R.M., Kikodze, D., Chiboshvili, M., & Khetsuriani, L. 2005. UNPALATABLE PLANTS PROTECT NEIGHBORS FROM GRAZING AND INCREASE PLANT COMMUNITY DIVERSITY. Ecology 86: 1856–1862. Fisher, R.J., Sawa, B., & Prieto, B. 2018. A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada. Remote Sensing of Environment 218: 201–206. GILL, R.M.A. 1992. A Review of Damage by Mammals in North Temperate Forests: 1. Deer. Forestry: An International Journal of Forest Research 65: 145–169. HASHIMOTO, Y., FUJIKI, D. (2014)List of food plants and unpalatable plants of sika deer (Cervus nippon) in Japan. (2014). Humans and Nature, 25, 133–160. HASHIMOTO, Y., ISHIMARU, K., KURODA, A., MASUNAGA, S., & YOKOTA, J. 2012. Effects of Mowing Resumption on Recovery and Richness of Grassland Plant Species in Abandoned Grasslands Dominated by Dwarf Bamboo. Landscape Research Japan Online 5: 95. Horsley, S.B., Stout, S.L., & DeCalesta, D.S. 2003. WHITE-TAILED DEER IMPACT ON THE VEGETATION DYNAMICS OF A NORTHERN HARDWOOD FOREST. Ecological Applications 13: 98–118. Inatomi, Y., Uno, H., & Iijima, H. 2017. Effects of Sika Deer (Cervus nippon) and Dwarf Bamboo (Sasa senanensis) on Trillium Populations in Akan National Park, Eastern Hokkaido, Japan. Plant Species Biology 32: 423–431. Kamada, M., 1994. Process of the establishment and maintenance of Sasa grassland in Tsurugi Mountains in Tokushima Prefecture, Shikoku, Japan, Bull. Tokushima Pref. Mus. 4, 97-113 Kogushi, S., Kamada, M., Hasegawa, K., 2005. Vegetation change relating to a change of management ad Ochiai-Pass in Higashi-Iyayama Village in Tokushima Prefecture, Shikoku, Japan, Bull. Tokushima Pref. Mus. 15, 1-20 Maake, R., Mutanga, O., Chirima, G., & Sibanda, M. 2023. Quantifying Aboveground Grass Biomass Using Space-Borne Sensors: A Meta-Analysis and Systematic Review. Geomatics 3: 478–500. Milchunas, D.G., & Noy-Meir, I. 2002. Grazing refuges, external avoidance of herbivory and plant diversity. Oikos 99: 113–130. Nakashizuka, T. 1988. Regeneration of beech (Fagus crenata) after the simultaneous death of undergrowing dwarf bamboo (Sasa kurilensis). Ecological Research 3: 21–35. Niwa, H., Dai, G., Ogawa, M., & Kamada, M. 2023. Development of a Monitoring Method Using UAVs That Can Detect the Occurrence of Bark Stripping by Deer. Remote Sensing 15: 644. Nuttle, T., Ristau, T.E., & Royo, A.A. 2014. Long-term biological legacies of herbivore density in a landscape-scale experiment: forest understoreys reflect past deer density treatments for at least 20 years. Journal of Ecology 102: 221–228. OKUBO, K. 2002. The Present State in the Study of Biological Diversity on Semi-natural Grassland in Japan(Activities of Biodiversity Conservation on Semi-natural Grassland in Japan). Japanese Journal of Grassland Science 48: 268–276. Otsu, C., Iijima, H., & Nagaike, T. 2019. Plant community recovery from intense deer grazing depends on reduction of graminoids and the time after exclosure installation in a semi-natural grassland. PeerJ 7: e7833. da Rocha, N.S., Veettil, B.K., Ward, R.D., Costi, J., & Rolim, S.B.A. 2023. Remote sensing of grasslands in the South American Pampas (scientometrics analysis). Land Degradation and Development 34: 2723–2734. Rooney, T.P. 2009. High white-tailed deer densities benefit graminoids and contribute to biotic homogenization of forest ground-layer vegetation. Plant Ecology 202: 103–111. Rooney, T., & Dress, W. 1997. Species loss over sixty-six years in the ground-layer vegetation of heart’s content, and old-growth forest in Pennsylvania, USA. Natural Areas Journal 17: 297–305. Rooney, T.P., & Waller, D.M. 2003. Direct and indirect effects of white-tailed deer in forest ecosystems. Forest Ecology and Management 181: 165–176. SAKANOUE, S. 2001. Long-term Trends in Miscanthus sinensis Grassland Vegetation : a 20-year Field Observation(Long-term Ecological Research in Grassland). Japanese Journal of Grassland Science 47: 430–435. SAKANOUE, S., FUKUDA, E., OGAWA, Y., OKAMOTO, K., & KITAHARA, N. 1995. Successional Process in a Sasa nipponica Grassland through Summer Grazing by Beef Cattle. Japanese Journal of Grassland Science 40: 443–447. Takahashi, K., Uehara, A., & Takatsuki, S. 2013. Food Habits of Sika Deer at Otome Highland, Yamanashi, with Reference to Sasa nipponica. Mammal Study 38: 231–234. Takatsuki, S., Ishikawa, S., & Higa, M. 2021. Altitudinal variation in sika deer food habits on Mt. Miune, Shikoku, Japan. JAPANESE JOURNAL OF ECOLOGY 71: 5. TAKATSUKI, S., & UEHARA, A. 2021. Cause of vegetation changes to a Miscanthus sinensis community in Otome Highland, Yamanashi, central Japan. Vegetation Science 38: 81–93. Tanaka, Y., Takatsuki, S., & Takayanagi, A. 2008. Decline of Sasa palmata community by grazing of Sika deer (Cervus nippon) at Ashiu Research Forest Station. Forest Research, Kyoto 77: 13–23. Tanentzap, A.J., Burrows, L.E., Lee, W.G., Nugent, G., Maxwell, J.M., & Coomes, D.A. 2009. Landscape-level vegetation recovery from herbivory: progress after four decades of invasive red deer control. Journal of Applied Ecology 46: 1064–1072. Weisberg, P.J., Bonavia, F., & Bugmann, H. 2005. Modeling the interacting effects of browsing and shading on mountain forest tree regeneration (Picea abies). Ecological Modelling 185: 213–230. WISDOM, M.J., VAVRA, M., BOYD, J.M., HEMSTROM, M.A., AGER, A.A., & JOHNSON, B.K. 2006. Understanding Ungulate Herbivory—Episodic Disturbance Effects on Vegetation Dynamics: Knowledge Gaps and Management Needs. Wildlife Society Bulletin 34: 283–292. YAMAMOTO, Y. 2001. Succession and Various Vegetation of Grassland(Long-term Ecological Research in Grassland). Japanese Journal of Grassland Science 47: 424–429. YAMAMOTO, Y., SAITO, Y., KIRITA, H., HAYASHI, H., & NISHIMURA, N. 1997. Ordination of Vegetation of Miscanthus-type Grassland under the Some Artificial Pressure. Japanese Journal of Grassland Science 42: 307–314. Yamamoto, Y., Saito, Y., Kirita, H., Takahashi, S., & Kitahara, N. 2007. Difference in Vegetational Changes in Miscanthus-type Grassland Following Cutting and Burning Treatments. Japanese Journal of Grassland Science 53: 28–30. Yokoyama, M., Kaji, K., & Suzuki, M. 2000. Food habits of sika deer and nutritional value of sika deer diets in eastern Hokkaido, Japan. Ecological Research 15: 345–355. Zhang, X., Bao, Y., Wang, D., Xin, X., Ding, L., Xu, D., Hou, L., & Shen, J. 2021. Using uav lidar to extract vegetation parameters of inner mongolian grassland. Remote Sensing 13: 1–18. Table 1 List of species in the survey Table 2 Test results for environmental factors in the NMDS Table 3 Correlations between the S. hayatae cover 2022 and M. sinensis cover 2022 at the study sites and the grid tabulations were analyzed for each 10-cm interval Fig. 1 Map of the study site Fig. 2 Distribution of survey sites and vectors of environmental factors. See Table 2 for the environmental factor codes. Fig. 3 Distribution of nine classes classified from the NMDS results Fig. 4 Distribution of species and vectors of environmental factors. Red circles, endangered species; blue circles, unpalatable species of C. nippon . See Table 2 for environmental factor codes. Fig. 5 Vegetation height of S. hayatae and M. sinensis Fig. 6. Measured S. hayatae cover and S. hayatae density estimated from the 3D point cloud model. Fig. 7 Measured M. sinensis cover and M. sinensis density estimated from the 3D point cloud model. Supplementary Material File (image1.emf) Download 35.06 KB File (image2.emf) Download 8.92 KB File (image3.emf) Download 49.38 KB File (image5.emf) Download 115.12 KB File (image7.emf) Download 242.36 KB Information & Authors Information Version history V1 Version 1 17 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cervus nippon miscanthus sinensis sasa hayatae browsing grassland Authors Affiliations Hideyuki Niwa 0000-0002-8199-3248 [email protected] Kyoto University of Advanced Science Faculty of Bioenvironmental Science View all articles by this author Guihang Dai Tokushima University View all articles by this author Midori Ogawa Tokushima University View all articles by this author Mahito Kamada Tokushima University View all articles by this author Metrics & Citations Metrics Article Usage 245 views 103 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hideyuki Niwa, Guihang Dai, Midori Ogawa, et al. Understanding 20 years of vegetation change in grasslands by focusing on changes in the cover of Sasa hayatae and Miscanthus sinensis. Authorea . 17 January 2025. DOI: https://doi.org/10.22541/au.173707563.38061276/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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