ライフサイエンスコーパス: vegetation

1 e on land" (SDG 15) via clearance of natural vegetation.

2 gions mainly due to the distribution of land vegetation.

3 assess the coherence of regional changes in vegetation.

4 climate variation and controlled by semiarid vegetation.

5 imodal distribution of structural aspects of vegetation.

6 ased, reaching similar levels to undisturbed vegetation.

7 d at 0.1 to indicate the presence/absence of vegetation.

8 ay rate may be a useful index for monitoring vegetation.

9 ts slow and steepen in the presence of green vegetation.

10 rk, which combines a designed hillscape with vegetation.

11 poverty alleviation, and changes in natural vegetation.

12 g to consumption of C(3) and mixed C(3)-C(4) vegetation.

13 ong-term atmospheric carbon sequestration in vegetation.

14 stocks, and, carbon sequestration by in situ vegetation.

15 isk for herbicide spray drift to surrounding vegetation.

16 grazers could directly consume propagules on vegetation?

17 ate-driven expansion of Herbaceous and Shrub vegetation (+7.4 +/- 2.0%) in the Arctic biome.

18 we found these fires burnt ~97,000 km(2) of vegetation across southern and eastern Australia, which

19 Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and

20 er soil water is needed to support increased vegetation activity, especially during the late growing

21 ax) and used to represent the seasonality of vegetation activity.

22 Vegetation and atmospheric water vapor also had a profou

23 te change and human land use co-regulate the vegetation and carbon cycles of a tropical lagoon system

24 e temperatures were determined from terrain, vegetation and ground properties using energy balance eq

25 f modeling experiments, we show that reduced vegetation and increased dust loads during the Green Sah

26 ted with significant nationwide increases in vegetation and its production.

27 d endocarditis given their ability to detect vegetation and perivalvular complications.

28 ht sites, each having paired plots of native vegetation and rain-fed croplands, and half the sites ha

29 le organic hydrocarbons commonly produced by vegetation and released into the atmosphere.

30 esis and how these processes are affected by vegetation and seasonality.

31 Vegetation and soil are key sources of airborne microbio

32 variability of T/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass

33 o track primary producer biomass (carbon) in vegetation and soil pools, and to track prevalence and t

34 Parent material, climate, vegetation and topography predict, collectively, 24 time

35 equally explained by local factors (soil and vegetation) and geospatial distance (11.5% and 11.9% res

36 olicies on biodiversity (indicated by native vegetation) and two ecosystem services (carbon storage,

37 outline complex links between local climate, vegetation, and ecosystem-atmosphere interactions, with

38 resulted in pronounced floristic, structural vegetation, and fuel load changes.

39 ing DOC-contributing factors, e.g., emergent vegetation, and supporting pH-increase processes, e.g.,

40 apture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur

41 in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to th

42 hazards like tsunamis, natural elements like vegetation are often combined with designed elements lik

43 er significant losses of life, property, and vegetation are sometimes conducted in the absence of nua

44 d affect water balance components in natural vegetation are still lacking at the plot scale.

45 abitats are driven by monodominance of woody vegetation as well as soil acidification by EM fungi, wh

46 especially if their food source is close to vegetation, as is the case for gleaning bats and nectar-

47 Here we show that vegetation at high latitudes enhances the Arctic amplifi

48 did show a tendency to minimize use of open vegetation at specific times of the day, overall we high

49 ogical partitioning and the strength of land-vegetation-atmosphere interactions at the catchment scal

50 versity and store large amounts of carbon in vegetation biomass.

51 ic as often assumed, but may appear so where vegetation boundaries are already sharp.

52 Vegetation buffers local diurnal land surface temperatur

53 more pollen limited in natural than managed vegetation, but the reverse is true for plants pollinate

54 es in vegetation composition and in soil and vegetation C and P stocks.

55 Calluna vulgaris, and led to a reduction in vegetation C stocks.

56 ood in marine systems, but cues from coastal vegetation can provide sensory information guiding aquat

57 The CZ lies between the top of the vegetation canopy and fresh, chemically unaltered bedroc

58 idence suggests that, to realistically model vegetation carbon allocation under increased climatic st

59 hat numerous climate-driven abrupt shifts in vegetation carbon are projected in a high-resolution mod

60 fidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage

61 ss the scenarios, 81% of abrupt increases in vegetation carbon have increasing autocorrelation and 74

62 represent a large proportion of the world's vegetation carbon pool.

63 In one scenario, 89% of abrupt increases in vegetation carbon show increasing autocorrelation and va

64 onal variation in plant species richness and vegetation carbon stock can be substantial, and may be r

65 e globally reduces the tree covered area and vegetation carbon storage by 10%.

66 rs controlling tropical forest diversity and vegetation carbon storage could be critical for predicti

67 n diameter) ranged from 69 to 127 ha(-1) and vegetation carbon storage ranged from 114 to 200 t ha(-1

68 Therefore, aboveground vegetation carbon storage typically differs between geog

69 lled by climate and soil water availability, vegetation carbon storage was strongly related to wood d

70 factors affect tropical forest diversity and vegetation carbon storage.

71 ariance beforehand, whereas for decreases in vegetation carbon these figures are 56% and 47% respecti

72 ed with increases (rather than decreases) in vegetation carbon, show the greatest potential for early

73 at Britain's land area show abrupt shifts in vegetation carbon.

74 hese differences to different definitions of vegetation change and effects of anthropogenic land use,

75 Our estimates of fire-induced vegetation change are lower than previous studies.

76 We aim to test to what extent vegetation change through time is associated with change

77 The rates of vegetation change varied significantly among sampling lo

78 Furthermore, vegetation changes also cause shifts in the chemical spe

79 guish between the potential drivers of these vegetation changes.

80 on rates in relation to historical land use, vegetation characteristics, and geophysical attributes.

81 ct has found limited applications for remote vegetation characterization.

82 ate variables vary geographically and across vegetation classes?

83 nterfactual biodiversity losses (unregulated vegetation clearing).

84 ompromised due to degraded conditions (e.g., vegetation clearing).

85 to missing anthropogenic forcing and two-way vegetation-climate feedback effect in simulations.

86 r adult cheetahs and cubs in areas with high vegetation complexity on both seasonal and lifetime scal

87 mated lion encounter risk, prey density, and vegetation complexity within their home range, on short-

88 es of predation risk, prey availability, and vegetation complexity, on mesopredator survival.

89 direct effects and indirect effects through vegetation composition and biomass alterations.

90 plication we quantified long-term changes in vegetation composition and in soil and vegetation C and

91 nd growing season duration, microtopography, vegetation composition, and ultimately, carbon function

92 ly when combined with P, strongly influenced vegetation composition, favouring grasses over Calluna v

93 showed persistent effects of N and N + P on vegetation composition, whereas effects of P alone were

94 e was also characterized by major changes in vegetation composition, which altered the environment oc

95 bal carbon (C) budget, nutrient cycling, and vegetation composition.

96 (NDVI), enhanced vegetation index (EVI), and vegetation continuous fields (VCF) across buffers of 500

97 atches and semi-natural treed elements (e.g. vegetation corridors).

98 with Leucocytozoon increased with increasing vegetation cover (NDVI) and moisture levels (NDWI), wher

99 n the warming Arctic, depending on the local vegetation cover and the climate dynamics.

100 infection rates, landscape features, such as vegetation cover and water body availability, play a sig

101 y reported reduced soil erosion or increased vegetation cover but lower water availability, although

102 Higher investment increased natural vegetation cover in some biomes but increased losses in

103 otope ratios as a proxy for changes in woody vegetation cover over time in response to fluctuations i

104 ly when comparing responses across different vegetation cover types.

105 Here we integrate plot-scale vegetation data with detailed climate and snow informati

106 Restoration by planting vegetation decreased the Q(10) of SOM mineralization as

107 Using a reduced-complexity form of the vegetation demographic model the Functionally Assembled

108 g the scientific understanding of changes in vegetation demographics and disturbances.

109 ationship between the thermal decay rate and vegetation depends on many factors including vegetation

110 was significantly higher for TEE than CT for vegetation detection, 94% (95% CI: 92%, 96%) (363 of 383

111 urysm whereas TEE gives superior results for vegetation detection, leaflet perforation, and paravalvu

112 This is because proxies to track vegetation development with daily coverage at the ecosys

113 Basic information about subnival vegetation distribution and rates of ecosystem change ar

114 orders of magnitude smaller than expected if vegetation distribution remained in equilibrium with cli

115 water usage, suggesting that irrigated urban vegetation drives the biospheric signal we observe.

116 Our results indicate that vegetation-dust climate feedbacks from Sahara drying may

117 ory experiments, and modelling of insect and vegetation dynamics, and focuses on how drought affects

118 upport increasingly realistic simulations of vegetation dynamics.

119 s for plant reproductive patterns and global vegetation dynamics.

120 urce of uncertainty in predictions of future vegetation dynamics.

121 ar responses commonly occur where removal of vegetation exceeds 80% (a level that occurs across nearl

122 ur analysis reveals a broad pattern of woody vegetation expansion into savannas and densification wit

123 but the strength and direction of impacts of vegetation expansion remain unknown.

124 Further, vegetation feedbacks substantially affect annual precipi

125 ly occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in

126 Vegetation-fire feedbacks are important for determining

127 the 2020 lockdown period and attribute it to vegetation fires.

128 n misunderstandings about Amazonian climate, vegetation, fires and the deforestation process to help

129 We modelled vegetation for glacial climates under different levels o

130 ental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites)

131 red change in the spatial extent of subnival vegetation from 1993 to 2018.

132 rage, respectively, compared to their native vegetation (grassland or woodland) pairs, and irrigated

133 ary guilds for phenological coupling between vegetation greenness and seasonal bird migration within

134 Phenological synchronization with vegetation greenness by migratory birds in other dietary

135 us bird species often track the phenology of vegetation greenness during spring migration.

136 an visits during the non-growing season, and vegetation greenness during the growing season.

137 sociations with a remotely sensed measure of vegetation greenness for 230 North American migratory bi

138 Vegetation greenness has increased across much of the gl

139 Space-borne observations of vegetation greenness show a large increase with time ove

140 Northwest, USA, as the percent reduction in vegetation greenness under droughts relative to baseline

141 ates (CONUS), we first detected the onset of vegetation greenup using the time series of the daily tw

142 of -4.75 ppm per decade (p < .05) during the vegetation growing season (May through October), suggest

143 bedrock geochemistry could exert effects on vegetation growth in karst regions and highlights that t

144 The enhanced N limitation on vegetation growth is driven by the joint effects of elev

145 the metal sheeting was absent and where the vegetation had overgrown around the fence, hence allowin

146 tes dominated by arbuscular mycorrhizal (AM) vegetation harbor relatively more AM fungi, bacteria, fu

147 ibiting greater selection for areas near low-vegetation height seismic lines in areas with low densit

148 -monsoon moisture for growth of the dominant vegetation, Himalayan birch Betula utilis and Himalayan

149 iving mechanisms behind existing patterns of vegetation hydraulic traits and community trait diversit

150 nd forests and woodlands shifted to nonwoody vegetation in 10% of them.

151 We quantified the drought sensitivity of vegetation in the Pacific Northwest, USA, as the percent

152 eric component likely originating from urban vegetation, including turf and trees.

153 degrees C in July), an increase in enhanced vegetation index (+0.10 in July), and 81% higher maize y

154 difference vegetation index (NDVI), enhanced vegetation index (EVI), and vegetation continuous fields

155 e time series of the daily two-band Enhanced Vegetation Index (EVI2) observed from the AVHRR Long-Ter

156 Residential normalized difference vegetation index (NDVI) and species richness index (SRI)

157 We calculated Normalized Difference Vegetation Index (NDVI) using 500 m radii around residen

158 f greenspace including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)

159 ith satellite measured normalized difference vegetation index and gross primary production shows that

160 The normalized difference vegetation index as a measure of food production and soc

161 ce reflectance-derived Normalized Difference Vegetation Index product was thresholded at 0.1 to indic

162 flat sites, increasing normalized difference vegetation index, and solar radiation all significantly

163 .083 degree resolution normalized difference vegetation indices (NDVI) over a 10 year period.

164 than those achieved by standard SIs, and by vegetation indices commonly used to predict agronomic tr

165 The simulated trends based on climate and vegetation indices show consistent results with some dif

166 The projected climate and vegetation indices show these trends will continue in 20

167 in photosynthetic activity using color-based vegetation indices.

168 ies of the SD and ArcTG based on climate and vegetation indices.

169 ions in hydrology, sediment composition, and vegetation influence hot spots of P retention throughout

170 Land vegetation is currently taking up large amounts of atmos

171 burns that arise in the dormant season when vegetation is desiccated, and soil moisture is high.

172 e manifestation of climate warming on tundra vegetation is highly dependent on the evolution of snow

173 bird species, protecting remnants of native vegetation is still of paramount importance for biodiver

174 ns (classified as abscess or pseudoaneurysm, vegetation, leaflet perforation, and paravalvular leakag

175 d transesophageal echocardiography delineate vegetation location and size, assess for paravalvular ex

176 ons of decreased water availability, using a vegetation model (LPX) driven by Atmosphere-Ocean couple

177 resolution using an enhanced dynamic global vegetation model and comprehensive land cover change dat

178 We test the ability of a vegetation model to simulate C cycling and community com

179 ion Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity.

180 NPP was simulated by MC2, a dynamic global vegetation model, driven by five climate projections for

181 physiology, disturbance ecology, mechanistic vegetation modeling, large-scale ecological observation

182 owever, most traits relevant for ecology and vegetation modelling are characterized by continuous int

183 ntal traits for plant functional ecology and vegetation modelling.

184 Current dynamic global vegetation models (DGVMs) are mainly photosynthesis-driv

185 ing-down, with an ensemble of dynamic global vegetation models (DGVMs) coupled with an atmospheric tr

186 valuate AGB estimated by nine dynamic global vegetation models (DGVMs).

187 ed to model photosynthesis in dynamic global vegetation models (E(aV) and E(aJ) ) show no response to

188 Process-based vegetation models attempt to represent the wide range of

189 However, most vegetation models currently lack any representation of s

190 Vegetation models encapsulate our understanding of fores

191 ra-annual wood formation processes in global vegetation models is vital for assessing climate change

192 Capturing these patterns in global vegetation models requires better mechanistic representa

193 Trait-enabled global vegetation models that explicitly include variation in b

194 study, we use simulations from seven global vegetation models to provide the first multi-model estim

195 Climate-driven vegetation models typically predict that this tropical f

196 and should be considered in next-generation vegetation models, particularly in the context of global

197 ng in current implementations of large-scale vegetation models, the under-representation of insect-in

198 ntation of growth responses to soil water in vegetation models.

199 d opening the door to the development of new vegetation models.

200 ire acknowledging the role of snow in tundra vegetation models.

201 ange and predictive abilities of mechanistic vegetation models.

202 trates the need to represent fire in dynamic vegetation models.

203 l to be incorporable into predictive dynamic vegetation models.

204 as a calibration target for next-generation vegetation models.

205 We find that vegetation N limitation becomes stronger despite the inc

206 reeminent role in regulating the feedback of vegetation on the soil water budget of salt-affected bas

207 pe data indicates that the expansion of C(4) vegetation opened up new niche opportunities, probably a

208 use, the distribution of wolf-killed elk and vegetation openness.

209 l scale, estimated using satellite microwave vegetation optical depth (VOD) observations.

210 n relate to potentially extensive changes in vegetation or ecological systems.

211 plication could mitigate the effects of N on vegetation or increase C sequestration in this system.

212 rges and how it will respond to variation in vegetation or soil moisture remains unknown.

213 ort ~1% of the OC sequestered by terrestrial vegetation, our estimates suggest that 34 +/- 26% of the

214 have caused large-scale changes in semi-arid vegetation over the past 50 years.

215 uisition strategies drive soil processes and vegetation performance, but their effect on the soil mic

216 Warming climate and its impact on vegetation phenological trends have been widely investig

217 are likely to remain unchanged or improve as vegetation phenology also becomes earlier.

218 cies to encounter phenological mismatches as vegetation phenology responds to climate change.

219 primary consumers synchronize migration with vegetation phenology.

220 trigger an increasing trend of variation in vegetation phenology.

221 Vegetation photosynthesis contributes more than ecosyste

222 eatest at the warmest site due to persistent vegetation photosynthetic activity throughout the winter

223 ological cycle through climate-radiative and vegetation-physiological forcings.

224 Recent advances in vegetation physiology, disturbance ecology, mechanistic

225 increase in moisture and expansion of woody vegetation prior to modern deforestation, which could he

226 al realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs.

227 s relative to cows, whereas low SWE and poor vegetation productivity 1 year prior together increased

228 bon balance of ecosystems through changes in vegetation productivity and ecosystem carbon turnover ti

229 ng the importance of bedrock geochemistry on vegetation productivity based on a critical zone investi

230 rawn an increasingly clear picture of global vegetation productivity changes, global changes in tau(e

231 The enhanced vegetation productivity driven by increased concentratio

232 Although low vegetation productivity has been observed in karst regio

233 ter loss rate (RWLR), while RWLR can predict vegetation productivity more effectively than previous m

234 geochemistry contributes to the low karstic vegetation productivity remain unclear.

235 ng the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given si

236 Russia based on remotely sensed measures of vegetation productivity using Dynamic Habitat Indices (D

237 effect controlled by background climate and vegetation properties.

238 tions of 1 year of carbon residence times in vegetation (r(veg) ) and of 9 years in soil (r(soil) ).

239 ts a high correlation with other established vegetation-related observations.

240 cosystem structure are expected at levels of vegetation removal akin to those in the most intensively

241 Terrestrial vegetation removes CO(2) from the atmosphere; an importa

242 t uncertainties associated with intra-annual vegetation response to climate change.

243 Modelling vegetation responses to changes in rainfall is thus cruc

244 Broad-scale assessments of vegetation responses to drought are needed to anticipate

245 be considered separately to correctly assess vegetation responses to environmental change.

246 egacy effects are important when considering vegetation responses to extreme events.

247 pically use empirical functions to represent vegetation responses to soil drought.

248 e up to diversity changes using data from 68 vegetation resurvey studies of seminatural forests in Eu

249 Vegetation, root trait and soil surveys were carried out

250 e applied to estimate salinization level and vegetation salt tolerance at the basin scale, which woul

251 it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to cli

252 e crop domestication history and detect past vegetation shifts.

253 Regional and global vegetation simulations can be problematic when analysis

254 positive increase in the extent of subnival vegetation since 1993.

255 ation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habita

256 eling was used to evaluate the importance of vegetation, soil physico-chemical properties and microbi

257 Simulations performed with a dynamic air-vegetation-soil model (SoilPlusVeg) confirmed that these

258 are to some extent distinct from the natural vegetation soils, it is surprising that so many of the k

259 nto a complex structure composed of multiple vegetation species and functional groups?

260 Our findings highlight the capability of vegetation spectroscopy to rapidly and nondestructively

261 cted to help prevent catastrophic changes in vegetation states by identifying improved monitoring app

262 tem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact

263 ative contribution of the peak leaf area and vegetation stress intensity was highly variable among mo

264 lts show that the decay rate summarizes both vegetation structure and function and exhibits a high co

265 ntains suitable soil conditions (via diverse vegetation structure and periodic saturation) to support

266 To understand how vegetation structure controls these feedbacks, we quanti

267 r environment than today and a non-analogous vegetation structure in the Early Pleistocene.

268 ere are also technical challenges to mapping vegetation structure in unbiased ways.

269 de that ecosystems self-organize towards the vegetation structure with the greatest outgoing entropy

270 ally burned 102 plots, for which we measured vegetation structure, fuels, microclimate, ignition succ

271 he underlying processes and consequences for vegetation structure.

272 was closer to crop inputs than under native vegetation, suggesting that cultivation has led to faste

273 evidence across temporal and spatial scales (vegetation survey, stand structure analysis, dendrochron

274 -grazed creekheads, through their removal of vegetation that otherwise obstructs predator access, enh

275 tion for belowground allocation of carbon by vegetation that reconciles seemingly contradictory exper

276 The site also had more aquatic vegetation that restricted the flow, less dissolved oxyg

277 he coverage of natural, randomly-distributed vegetation to 80%.

278 he land available for compensation (existing vegetation to protect or cleared land to restore), and e

279 en and quantified the contribution of canopy vegetation to soil CO(2) fluxes and belowground producti

280 y in body size and regularly forage in dense vegetation, to investigate whether flying insects consid

281 y and functions due to increasing changes in vegetation traits under global change.

282 n the Baltic region that broadly differed in vegetation type and nutritional traits, such as mycorrhi

283 ater level (GWL), as well as modification of vegetation type, both of which potentially influence CH(

284 vegetation depends on many factors including vegetation type, size, water content, location, and loca

285 ed to soils, topography, climate, land cover/vegetation type, successional dynamics, time since fire,

286 (mean 5 y, range 1 to 23 y after mortality) vegetation-type conversion in multiple biomes across the

287 nthesis, and its variation across and within vegetation types is poorly understood, which hinders our

288 vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US,

289 the drought response behaviour of five broad vegetation types, based on a common garden dry-down expe

290 orage of SOC in estuarine wetlands with four vegetation types, including single Phragmites australis

291 e sensitivity exhibits clear patterns across vegetation types, multivariate climate change data revea

292 SOC decomposition or accumulation among four vegetation types, possibly due to differences in litter

293 ew 24 tree species occurring in five African vegetation types, varying from dry savanna to moist fore

294 ion might be much more widespread since land vegetation was absent.

295 s the ratio of carbon uptake to water use by vegetation, water-use efficiency (WUE) is a key ecosyste

296 wlands, where broadleaf species dominate the vegetation, we find consistent decreases in tree longevi

297 xpected climate change effects on Australian vegetation, we need to assess the vulnerability of Austr

298 of sediment released from behind incinerated vegetation, which can fuel catastrophic debris flows.

299 and the subsequent nighttime uptake of Hg by vegetation, which depletes Hg from the atmosphere.

300 unity physiological functioning across three vegetation zones: grass, transitional, and shrub in a co