ライフサイエンスコーパス: vegetation

1 (40-80 cm, below active root zone of native vegetation).

2 cultural production to restoration of native vegetation.

3 t and in dynamic model simulations of global vegetation.

4 ount the full vertical stratification of the vegetation.

5 f climate and the growth form and biomass of vegetation.

6 t magnetization, are insensitive to changing vegetation.

7 g the phyllosphere, i.e. the aerial parts of vegetation.

8 gnal that was previously absorbed by natural vegetation.

9 ation, thawing of permafrost, and changes in vegetation.

10 reservoirs on the shallow soil property and vegetation.

11 s to plants and thus enhancing the growth of vegetation.

12 ally overdispersed nest sites and associated vegetation.

13 ty for leaf-level CO2 assimilation in Arctic vegetation.

14 ange on phosphorus availability to grassland vegetation.

15 ced in summer through the uptake of Hg(0) by vegetation.

16 llowing and a short-range avoidance of dense vegetation.

17 R 1.89, 95% CI 1.07-3.35, p=0.028), clearing vegetation (1.89, 1.11-3.22, p=0.020), and having long g

18 iled habitats never reached standing unoiled vegetation after 72 h.

19 o the responses expected through the classic vegetation-albedo feedback mechanism.

20 herbs and shrubs replaced forest understory vegetation along a tidal flooding gradient.

21 topography, and parent material but changing vegetation along the forest-prairie ecotone in NW Minnes

22 ncorporating dendroecology data into coupled vegetation and climate models.

23 latile organic compounds from urban/suburban vegetation and corresponding ground-level ozone and part

24 solar insolation changes are varied but also vegetation and dust concentrations.

25 These changes imply major shifts in vegetation and ecosystem service delivery.

26 ons had enough precipitation to support rich vegetation and fauna.

27 We establish that substantial changes in vegetation and fire regime occurred approximately 70,000

28 fting cultivation, and regrowth of secondary vegetation and how much is forced by internal variabilit

29 red differences in surface soil C related to vegetation and land use history and determined that floo

30 Oscillation, which had a major influence on vegetation and local climate of European dust source reg

31 the first time that UCI is caused by lack of vegetation and moisture in non-urban areas relative to c

32 veloped numerical model, which fully couples vegetation and sediment-transport dynamics, to simulate

33 R and SR-RR was maintained for most types of vegetation and soil as well as for different methods of

34 the supply of pollen and biomarkers from the vegetation and soil OM pools to determine local habitat

35 ge on local terrestrial ecosystems and their vegetation and soil organic matter (OM) pools is often n

36 Forest vegetation and soils have been suggested as potentially

37 pe measurements in the atmosphere, snowpack, vegetation and soils support our finding that Hg(0) domi

38 Here we present the first global vegetation and terrestrial temperature reconstructions f

39 fen to act as a C sink by causing shifts in vegetation and thus reducing magnitude of maximum growin

40 d (0-40 cm, active root zone of native marsh vegetation), and deep SOM-derived mineralization (40-80

41 ased on ecological potential (i.e. potential vegetation), and often fail to account for ongoing chang

42 xity of impervious land surfaces, buildings, vegetation, and management.

43 atios, oxygen isotope ratios of phosphate in vegetation, and phosphatase enzyme activity in soil to s

44 unction migrated at different rates: Wetland vegetation appeared to be a leading indicator of marsh m

45 y heavy shade associated with intact natural vegetation are likely to provide conditions suitable for

46 ch centers, studies on jets interacting with vegetation are still rare.

47 o habitat loss for the locally dominant pine vegetation as the terraces drowned.

48 dered the most detrimental air pollutant for vegetation at the global scale, with negative consequenc

49 re acclimation could strongly affect coupled vegetation-atmosphere feedbacks in the global carbon cyc

50 er, in the Gulf of Mexico, the loss of marsh vegetation because of human-driven disturbances such as

51 mics model, to estimate potential changes in vegetation biomass and net primary production (NPP) at t

52 parameters, such as the amount of bare soil, vegetation biomass production and vegetation height, whi

53 bivory, and other disturbances on changes in vegetation biomass, community structure, and ecosystem f

54 ation, with available P increasing on native vegetation but decreasing on cropland.

55 erged as a new proxy for reconstructing past vegetation, but its taphonomy, source area and represent

56 Conifers eventually overtop the competing vegetation, but until they do, these systems could be pe

57 Here, we isolate the influence of vegetation by investigating magnetic properties of soils

58 iod 1997-2015, and quantified the BD-induced vegetation C loss, that is, C fluxes from live vegetatio

59 s much as 23% of the above- and belowground vegetation C stocks in Ecuadorian forests.

60 Here, we evaluate vegetation carbon turnover processes in GVMs participati

61 CHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combi

62 ntation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosyste

63 ting a spatial and temporal heterogeneity of vegetation change at the EOT in both hemispheres.

64 t land-use change was an important driver of vegetation change even able to counterbalance the effect

65 ude that the heterogeneous pattern of global vegetation change has been controlled by a combination o

66 re a globally extensive biome prone to rapid vegetation change in response to changing environmental

67 a meta-analysis, we quantified savanna woody vegetation change spanning the last century.

68 Vegetation change will mostly exacerbate low soil water

69 of two small Scottish lakes reflects a major vegetation change, using well-documented 20(th) Century

70 ican savannas are at high risk of widespread vegetation change.

71 inority of locations, climate change-induced vegetation changes may lead to a net increase in water a

72 er availability due to climate change alone, vegetation changes will counteract these increases due t

73 rivers of the distribution of tree cover and vegetation classes (defined by the modes of tree cover d

74 em hundreds of millions of years ago through vegetation-climate feedbacks.

75 Urban expansion and vegetation collectively impact GPP variations in these m

76 The potential of emissions from urban vegetation combined with anthropogenic emissions to prod

77 Poor vegetation conditions caused by heavy sheep grazing were

78 io (NBR), existing MODIS Land Cover (LC) and Vegetation Continuous Fields (VCF) products, and the Lan

79 st cover change during 2000-2010 using MODIS vegetation continuous fields images, to understand the s

80 CAFC map, a regional map derived from MODIS Vegetation Continuous Fields tended to underestimate for

81 ange, the impacts of climate change alone on vegetation cover and sediment mobility may be relatively

82 he role that wind, moisture availability and vegetation cover play in shaping dryland landscapes, rel

83 hic changes and the evolution of an emerging vegetation cover since the 1960s, due to widespread refo

84 electric conductivity in the soil zone; (b) vegetation cover type induced differences in vertical bu

85 Our results suggest land use and vegetation cover type, as opposed to rock properties, co

86 xes, biomass, streamflow and remotely sensed vegetation cover) and two state-of-the-art biospheric mo

87 verse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into spe

88 Fire frequency has a primary impact on vegetation cover, and, together with grazing pressure, p

89 Using vegetation data from 56 Sphagnum-dominated peat bogs acr

90 spatially explicit small-mammal trapping and vegetation data from the UHURU Experiment, a replicated

91 Here we subdivide satellite vegetation data into those from human-unaffected areas a

92 tchment is relatively intact but the rate of vegetation decline is high.

93 t is relatively intact and has a low rate of vegetation decline; (3) land-based actions are optimal w

94 ow that the prevalence of groundwater use by vegetation (defined as the number of samples out of a un

95 the representation of ecosystem ecology and vegetation demographic processes within Earth System Mod

96 During summer months, fires and vegetation-derived secondary organic carbon together oft

97 orological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed

98 nnative plant species, and the loss of woody vegetation due to drought may create a window of opportu

99 Vegetation dynamics and ecosystem processes, such as abo

100 in a warmer, drier climate, we characterized vegetation dynamics following severe fire in nine fire y

101 into their land components to better predict vegetation dynamics in a changing climate.

102 We apply ArcVeg, an arctic tundra vegetation dynamics model, to estimate potential changes

103 ather severity have altered fire regimes and vegetation dynamics.

104 cts of rising temperatures on high-elevation vegetation dynamics.

105 rd sex and age, prey size and vulnerability, vegetation, elevation, climate, and the immediate and lo

106 the production, fluxes and fate of PyC from vegetation fires.

107 spatial, spectral, and temporal dynamics of vegetation fluorescence complicate our ability to interp

108 We emphasize the dual role of vegetation for air quality and human health in cities du

109 All the main vegetation formations exhibited pre-rain green-up, by as

110 fts in the dominant ecological strategies of vegetation found across the Triassic-Jurassic transition

111 shows that negative direct effects of LMH on vegetation frequently propagate to suppress the abundanc

112 ted in marine sediments indicate constant C3 vegetation from approximately 24 Ma to 10 Ma, when C4 gr

113 e efficiency (WUE), is a useful indicator of vegetation function.

114 Thus, eCO2 has the potential to alter vegetation functioning in a periodically dry woodland un

115 However, vegetation gas exchange parameters derived from EC data

116 ditional agricultural practices with a clear vegetation gradient.

117 e, total winter precipitation, and detrended vegetation green-up dates indexed by the normalized diff

118 Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late an

119 mospheric subsidence in response to positive vegetation greenness anomalies are counter to the respon

120 We found that phenology cycle (changes in vegetation greenness) in urban areas starts earlier (sta

121 er, air temperature, vapor pressure deficit, vegetation greenness, and nitrogen at current and antece

122 sis of a general CO2-fertilization effect on vegetation growth and suggest that, so far unknown, sulp

123 nces to discover the potential influences on vegetation growth from the artificial topography.

124 st emissions in response to diminished Sahel vegetation growth, potentially contributing to the posit

125 all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water us

126 etrics studied (contiguity, circularity, and vegetation) have a statistically significant relationshi

127 erence vegetation index (NDVI), a measure of vegetation health and greenness; Landsat-derived impervi

128 bare soil, vegetation biomass production and vegetation height, while brown web trophic groups are mo

129 che models to include features common to all vegetation-height-structured competition for light under

130 ute to a predictive understanding of savanna vegetation heterogeneity.

131 es to identify what determines local savanna vegetation heterogeneity.

132 effect of disturbance on elevation dynamics, vegetation in half of the plots was subjected to freezin

133 n of microbial communities, possibly because vegetation in old parks have had a longer time to modify

134 These results suggest that changes in vegetation in response to climate change may exacerbate

135 ils, differing significantly from the native vegetation in root : shoot ratio and belowground biomass

136 aken into account when analysing the role of vegetation in the global carbon cycle.

137 Reduced rates of water use by the vegetation in the high CO2 treatment could be contributi

138 Combined these results indicate that the vegetation in these grassland systems is not very sensit

139 The gross primary production (GPP) of vegetation in urban areas plays an important role in the

140 -dose phage therapy killed 2.5 log CFUs/g of vegetations in 6 hours (P < .001 vs untreated controls)

141 highly synergistic, killing >6 log CFUs/g of vegetations in 6 hours and successfully treating 64% (n

142 cular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic

143 nets-occupied regions in Russia (27% greater vegetation increase without herbivores).

144 Using MODIS Enhanced Vegetation Index (EVI), we investigated greenness trend

145 f the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts tha

146 LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R(2 ) = 0.36 for canopy GPPSIF

147 etween remotely-sensed normalized difference vegetation index (NDVI) and abundance of small mammals t

148 o canopy loss, reduced Normalized Difference Vegetation Index (NDVI) and reduced recruitment.

149 ctroradiometer (MODIS) normalized difference vegetation index (NDVI) as a proxy for productivity duri

150 ia a satellite-derived normalised difference vegetation index (NDVI) based on the GPS coordinates of

151 d from remotely sensed Normalized Difference Vegetation Index (NDVI) within both 250 m and 500 m of p

152 ometer (MODIS)-derived normalized difference vegetation index (NDVI), a measure of vegetation health

153 es (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EV

154 Comparing the Normalized Difference Vegetation Index (NDVI), the Simple Ratio (SR), and the

155 en oxides], greenness [Normalized Difference Vegetation Index (NDVI)], and neighborhood walkability a

156 difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water i

157 lt vegetation indices, normalized difference vegetation index and normalized difference red edge, wer

158 tudy, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of

159 roductivity estimates (Normalised Difference Vegetation Index, NDVI) across the Svalbard Archipelago

160 p dates indexed by the normalized difference vegetation index.

161 rrestrial water storage (since 2002) and two vegetation indices (since 2004).

162 Vegetation indices (VIs) including the normalized differ

163 lculate N sufficiency index with the default vegetation indices and then to estimate N nutrition inde

164 Significant increases in remotely sensed vegetation indices in the northern latitudes since the 1

165 luated in comparison with the top performing vegetation indices selected from 51 tested indices.

166 irical approaches using the sensor's default vegetation indices, normalized difference vegetation ind

167 Surface vegetation is dominated by Warnstorfia fontinaliopsis, a

168 evalence and magnitude of groundwater use by vegetation is unknown.

169 Savanna vegetation is variable, and predicting how water, nutrie

170 ater as a resource in sustaining terrestrial vegetation is widely recognized.

171 The presence of vegetation, known to limit N2O emissions in tundra, did

172 horelines experienced heavy oiling including vegetation laid over under the weight of oil.

173 and temporal multi-angular observations, and vegetation light use efficiency was strong (r(2) = 0.64

174 To investigate the effect of vegetation loss on ecosystem N removal, we contrasted de

175 Green space and vegetation may play a protective role against urban viol

176 Policies to increase vegetation may provide opportunities for physical activi

177 ere feedbacks involving desiccated soils and vegetation might have played a role in driving the heat

178 e combined projections from a dynamic global vegetation model (DGVM) that simulates the distributions

179 n experiments of an individual-based dynamic vegetation model (i.e., LM3-PPA).

180 n global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM).

181 -ring studies, but agree with dynamic global vegetation model projections.

182 We extended a dynamic global vegetation model to simulate ecosystem response to presc

183 Dynamic global vegetation models (DGVM) exhibit high uncertainty about

184 high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against

185 for designing and evaluating dynamic global vegetation models (DGVMs).

186 Turnover concepts in state-of-the-art global vegetation models (GVMs) account for various processes,

187 ent in, for example, Earth system or dynamic vegetation models in order to provide a systematic asses

188 This knowledge can enrich "next-generation" vegetation models in which leaf temperature and water us

189 However, biogeographical theory and global vegetation models poorly represent recent forest die-off

190 cially given the lack of efficient dynamical vegetation models to evaluate forest tree cover changes

191 arameterizing the next generation of dynamic vegetation models.

192 restrial ecosystems by triggering widespread vegetation mortality.

193 These results demonstrate that vegetation near streets contributes substantially to sto

194 indirect, climate change-induced changes in vegetation on soil water availability.

195 lly based on diurnal variations in microwave vegetation optical depth (VOD), which is directly relate

196 at in fact there is no significant change in vegetation over the cold regions where warming is signif

197 of deep-buried SOM accumulated under native vegetation, P. australis invasion into a wetland could f

198 reen web trophic groups are mainly driven by vegetation parameters, such as the amount of bare soil,

199 ered substantially from most types of modern vegetation, particularly forests.

200 method to estimate CO2 exchange from intact vegetation patches under varying atmospheric CO2 concent

201 eading to the hypothesis that the xerophytic vegetation patches which presently harbor these populati

202 bean during 2000-2015, and analyzed trend of vegetation patches without LCLUC to give prominence to c

203 plant performance is reflected in observable vegetation patterning (i.e., spacing distance, density)

204 Large-scale regular vegetation patterns are common in nature, but their caus

205 Self-organized regular vegetation patterns are widespread and thought to mediat

206 the same instability type will show similar vegetation patterns even if the feedback mechanisms and

207 es, finding evidence that spotted and gapped vegetation patterns generated by ants, termites, and oth

208 On the other hand, many overdispersed vegetation patterns worldwide have been attributed to su

209 nsive peat deposits beneath the swamp forest vegetation (peat defined as material with an organic mat

210 examined the relationship between change in vegetation phenology and urban size, an indicator of urb

211 great use for developing improved models of vegetation phenology dynamics under future urbanization,

212 The influence of urbanization on vegetation phenology is gaining considerable attention d

213 ors to assess the impacts of urbanization on vegetation phenology.

214 Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and

215 monstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies o

216 ur understanding of the interactions between vegetation physiology and spectral characteristics to un

217 xamined the longest running set of permanent vegetation plots in the Fynbos of South Africa (44 y), f

218 sented using an ensemble exploring both land vegetation processes and physical climate feedbacks in a

219 oisture and ecological droughts, which drive vegetation productivity and composition, remain poorly u

220 owed that recurrent droughts deeply affected vegetation productivity throughout the observation perio

221 e link between the growing season length and vegetation productivity.

222 ground carbon density of tropical woody live vegetation, providing direct, measurement-based evidence

223 However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo m

224 evidence for the region's proposed positive vegetation-rainfall feedback on the seasonal to interann

225 th, potentially contributing to the positive vegetation-rainfall feedback.

226 teractions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albed

227 The first phase was mainly influenced by vegetation recovery after an eruption of the Tianchi vol

228 Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR

229 f nirS-type denitrifers indicated that marsh vegetation regulates the activity, rather than the abund

230 ion such that in the early evolution stages, vegetation removal results in gullying, but that refores

231 ently lack a systematic understanding of how vegetation responds to asymmetric seasonal environmental

232 Current models used for predicting vegetation responses to climate change are often guided

233 Our results show that vegetation restoration can improve pollination, suggesti

234 Vegetation restoration is a common tool used to mitigate

235 eld experiment to investigate the effects of vegetation restoration, specifically the removal of exot

236 was observed that correlated with discharge, vegetation, river morphology and water residence time.

237 hragmites australis roots deeper than native vegetation (Schoenoplectus americanus and Spartina paten

238 ing the detachment of a branch from suitable vegetation; "sculpting" of a terminal hook from the noda

239 Taller P. australis vegetation serves primarily as a visual obstruction for

240 caused by artificial topography reduced the vegetation sexual reproduction.

241 stem models that estimate future climate and vegetation show little agreement in Amazon simulations.

242 gmented landscapes that have more complex 3D vegetation showed greater functional connectivity and we

243 into an obstructed cross-flow, with emergent vegetation simulated with a regular array of cylinders.

244 ns of volatile organic compounds (VOCs) from vegetation simulated with MEGAN to quantify some of thes

245 horeline and mountain slopes and hence local vegetation, soil development and OM export to the lake s

246 where soils are P-impoverished, with diverse vegetation, soil, and parent material types and a wide r

247 s in soil C-N-P stoichiometry differed among vegetation, soil, parent material types, and spatial cli

248 Vegetation stands have a heterogeneous distribution of l

249 eenspace models and waveform lidar-generated vegetation strata (namely, grass, shrubs and trees).

250 els, as the latter assumes that all vertical vegetation strata are connected, which is rarely true.

251 abitat availability for fauna requiring open vegetation structure (such as migratory birds and foragi

252 therefore more likely to govern biomass and vegetation structure in Amazonia.Earth system model simu

253 d chronic disturbances interact to determine vegetation structure in savannas represents a challenge.

254 were distinct between sites that differed in vegetation structure or precipitation.

255 In agriculture, however, vegetation structure was more uniform, contributing to 7

256 Savanna vegetation structure was reasonably predictable, via a c

257 res reduced aerosol concentrations, modified vegetation structure, and increased the magnitude of the

258 ollected data on bird community composition, vegetation structure, and tree diversity across 120 site

259 eded regarding how human practices influence vegetation structure.

260 We conducted vegetation surveys, pitfall trapping of invertebrates, v

261 In modeling the Amazonian vegetation system, we include symmetric alpha-stable Lev

262 SedDNA recorded other changes in local vegetation that accompanied afforestation.

263 t-eating grazers eliminated drought-stressed vegetation that could otherwise survive and recover from

264 th that in adjacent soil inhabited by native vegetation that input labile litter, whereas the soils u

265 assuming a unidirectional COS flux into the vegetation that scales with GPP.

266 iously sequestered at depth under the native vegetation, thereby altering the function of a wetland a

267 Applied to the context of dryland vegetation, this principle predicts that different syste

268 getation C loss, that is, C fluxes from live vegetation to dead organic matter pools.

269 We show that the responses of semi-arid vegetation to ENSO occur in opposite directions, resulti

270 a new perspective on the response of global vegetation to environmental changes.

271 ansparency was increased, allowing submerged vegetation to penetrate deeper, and the habitat suitable

272 containing either unoiled or oiled laid over vegetation to represent a heavily impacted marsh habitat

273 nd-atmosphere models that couple terrestrial vegetation to the global carbon cycle.

274 erties, controls deep water drainage for the vegetation transition zone.

275 unoff flux, during repeated, climate-driven, vegetation turnovers.

276 Our analyses suggest that dominant vegetation type and soil type are important attributes i

277 ese conditions have the potential to lead to vegetation type change and altered carbon (C) dynamics.

278 r a lower CCC in the system due to extensive vegetation type conversion from forest to non-forest typ

279 nger survival, than Spartina or mixed plots, vegetation type had no effect on rates of accretion, ver

280 widespread mortality and a shift in dominant vegetation type in interior Alaska.

281 ive responses in the biosphere, depending on vegetation type.

282 widespread loss of native woody species and vegetation-type conversion.

283 ental conditions, our model predicts diverse vegetation types and trait mixtures, akin to observation

284 tural ecosystems with different soil depths, vegetation types, and climate gradients remains poorly u

285 f GSL between urban and rural areas over all vegetation types, considered in this study, is about 9 d

286 50 to +330 gCm(-2) across sites with diverse vegetation types, contrasting with the more constant sin

287 anges in soil stoichiometry are dependent on vegetation types, soil types, and spatial climate variat

288 For different vegetation types, the phenology response to urbanization

289 th distance from shore and among habitat and vegetation types.

290 nover of AMF with < 12% of VT present in all vegetation types.

291 The persistence of vegetation under climate change will depend on a plant's

292 flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree

293 three-dimensional (3D) connectivity in urban vegetation using waveform lidar technology that measures

294 nt assay, snail movement to standing unoiled vegetation was significantly lower in oiled chambers (oi

295 dering indirect effects of climate change on vegetation when assessing future soil moisture condition

296 areas that were more accessible, had sparser vegetation, where human population density was higher, a

297 the much taller S. salsa/P. australis mosaic vegetation whereas the duration of vigilance showed no s

298 lobal extent of animal-induced regularity in vegetation-which can modulate other patterning processes

299 muskeg with wood fibers derived from native vegetation with the addition of inorganic silicate precu

300 ceeding 85% within three largest bioclimatic vegetation zones (northern, middle, and southern taiga),