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3,105 results for “Vegetation =”
The trajectories of vegetative structure and soil microbial function diverged across a fire chronosequence of the boreal forests in Northeast China
<p>The role of boreal forest to ameliorate the effect of global climate change largely depends on the regeneration of postfire forests in northeast China. The postfire recovery of boreal forest can be evaluated by the aboveground vegetative structure and soil microbial function. In present study, a 50-year fire chronosequence was established, and the biomass of forbs, shrub and woody plant was separately weighted to assess their contribution to the whole community with the year since fire (YSF). Simultaneously, soil biophysical properties were measured for stands in different time period after fire. Soil microbial functions, i.e., growth efficiency (GE) and carbon use efficiency (CUE), were calculated basing on ecoenzymatic and soil nutrient stoichiometry. In terms of vegetative structure, forbs' proportion decreased from 75% to 1.5%, but the proportion of woody plant increased from 0.04% to 70% across this fire chronosequence. In contrast, soil microbial function reached the highest value in 15 YSF and then began to decrease. As an important variable, soil metal content, particularly the calcium content, showed a positive correlation with woody plant biomass and a negative with soil microbial function. Furthermore, soil metal content was significantly increased in the late stage of this fire chronosequence. Overall, the present work highlighted that the time period of 15 YSF and 31 YSF was a hallmark stage for aboveground vegetative structure and soil microbial function to change in different trends, and the calcium content may partly account for these two divergent trajectories.</p>
Data for: Saturation response of enhanced vegetation productivity attributes to intricate interactions
<p>Evidence for the multifaceted responses of terrestrial ecosystems has been shown by the weakening of CO<sub>2</sub> fertilization-induced and warming-controlled productivity gains. The intricate relationship between vegetation productivity and various environmental controls still remains elusive spatially. Her,e several inherent preponderances make China a natural experimental setting to investigate the interaction and relative contributions of five drivers to gross primary productivity for the period from 1982 to 2018 (i.e., elevated atmospheric CO<sub>2</sub> concentrations, climate change, nutrient availability, anthropogenic land use change, and soil moisture) by coupling multiple long-term datasets. Despite a strikingly prominent enhancement of vegetation productivity in China, it exhibits similar saturation responses to the aforementioned environmental drivers (elevated CO<sub>2</sub>, climatic factors, and soil moisture). The CO<sub>2</sub> fertilization-dominated network explains the long-term variations in vegetation productivity in humid regions, but its effect is clearly attenuated or even absent in arid and alpine environments controlled by climate and soil moisture. Divergence in interactions also provides distinct evidence that water availability plays an essential role in limiting the potential effects of climate change and elevated CO<sub>2</sub> concentrations on vegetation productivity. Unprecedented industrialization and dramatic surface changes may have breached critical thresholds of terrestrial ecosystems under the diverse natural environment and thus forced a shift from a period dominated by CO<sub>2</sub> fertilization to a period with non-linear interactions. These findings suggest that future benefits in terrestrial ecosystems are likely to be counteracted by uncertainties in the complicated network, implying an increasing reliance on human societies to combat potential risks. Our results therefore highlight the need to account for the intricate interactions globally and thus incorporate them into mitigation and adaptation policies.</p>
FIGURE 1. Justicia tanalensis. A–B. Habit. C. Vegetative part. D–E. Inflorescences. F. Flower buds. G–H. Flowers. I in Reappraisal and lectotypification of Justicia tanalensis S. Moore (Acanthaceae), rediscovered from central Madagascar more than 100 years since the last collection
FIGURE 1. Justicia tanalensis. A–B. Habit. C. Vegetative part. D–E. Inflorescences. F. Flower buds. G–H. Flowers. I. Capsule. Photographed by G.W. Hu and G.E. Onjalalaina.
FIGURE 1. Leptomischus multiflorus, vegetative morphology. A, B in Leptomischus multiflorus (Argostemmateae: Rubiaceae), a new species from southern Vietnam
FIGURE 1. Leptomischus multiflorus, vegetative morphology. A, B. Habit (note weak anisophylly). C. Stem node with stipule and bases of two leaves. From Nuraliev, Lyskov NUR 3486. Photos by M.S. Nuraliev.
FIGURE. Results of discriminant function an alysis (DFA) for C. brizoides (br), C. curvata (cu) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LB = 0.63, LN = -0.93, WN = 0.94, LN/WN = 1.13, FGL = -0.62. Loadings for the second axis: WW = -0.76, LB = 1.51, LN = 2.21, WN = -1.79, LN/WN = -1.48, FGL = 0.55, FGW = -0.51. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CLL = -0.58. Loadings for the second axis: CW = -0.72, IL = -0.78. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function an alysis (DFA) for C. brizoides (br), C. curvata (cu) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LB = 0.63, LN = -0.93, WN = 0.94, LN/WN = 1.13, FGL = -0.62. Loadings for the second axis: WW = -0.76, LB = 1.51, LN = 2.21, WN = -1.79, LN/WN = -1.48, FGL = 0.55, FGW = -0.51. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CLL = -0.58. Loadings for the second axis: CW = -0.72, IL = -0.78.
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. colchica (co) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LW = -0.53, LN = -1.53, WN = 2.40, LN/WN = 2.13. Loadings for the second axis: LN = -0.56. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): IL = 0.62. Loadings for the second axis: CW = 0.86, CLL = 0.78, CLW = -0.65. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. colchica (co) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LW = -0.53, LN = -1.53, WN = 2.40, LN/WN = 2.13. Loadings for the second axis: LN = -0.56. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): IL = 0.62. Loadings for the second axis: CW = 0.86, CLL = 0.78, CLW = -0.65.
FIGURE. Results of discriminant function analysis (DFA) for the vegetative characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2. Loadings for the first axis (only absolute values>0.50 are given): SN = 0.57. Loadings for the second axis: CL = -0.91, IL = 0.54. Loadings for the third axis: CW = 0.62, CLL = -0.52, CLW = -0.89, IL = 0.51. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides, re—C. repens. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for the vegetative characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2. Loadings for the first axis (only absolute values>0.50 are given): SN = 0.57. Loadings for the second axis: CL = -0.91, IL = 0.54. Loadings for the third axis: CW = 0.62, CLL = -0.52, CLW = -0.89, IL = 0.51. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides, re—C. repens.
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. pseudobrizoides (ps) and C. brizoides (br). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL/UW = 0.79, LN = -0.72, WN = 1.33, LN/WN = 1.45, FGL = -0.72. Loadings for the second axis: UW = -1.37, UL/UW = -0.55, LN = 2.13, WN = -2.07, LN/WN = -1.45. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.76, IL = 0.82. Loadings for the second axis: CL = 0.57, SN = 0.68, LSL = 0.79. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. pseudobrizoides (ps) and C. brizoides (br). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL/UW = 0.79, LN = -0.72, WN = 1.33, LN/WN = 1.45, FGL = -0.72. Loadings for the second axis: UW = -1.37, UL/UW = -0.55, LN = 2.13, WN = -2.07, LN/WN = -1.45. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.76, IL = 0.82. Loadings for the second axis: CL = 0.57, SN = 0.68, LSL = 0.79.
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. brizoides (br) and C. praecox (pr). Characters abbreviated as in Table 2): A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL = 0.53, UL/UW = -0.56, LN = 1.37, WN = -2.23, LN/WN = -2,03, FGL = 0.56. Loadings for the second axis: UL/UW = 0.79, LW = 0.61, LN = -0.87, WN = 0.90, LN/WN = 0.87. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.54, IL = 0.72. Loadings for the second axis: CLL = -0.73. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. brizoides (br) and C. praecox (pr). Characters abbreviated as in Table 2): A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL = 0.53, UL/UW = -0.56, LN = 1.37, WN = -2.23, LN/WN = -2,03, FGL = 0.56. Loadings for the second axis: UL/UW = 0.79, LW = 0.61, LN = -0.87, WN = 0.90, LN/WN = 0.87. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.54, IL = 0.72. Loadings for the second axis: CLL = -0.73.
Effects of habitat types on the dynamic changes of allocation in carbon and nitrogen storage of vegetation-soil system in sandy grasslands
<p>The progressive restoration of degraded vegetation in semiarid and arid desertified areas undoubtedly formed different habitat types. The most plants regulate their growth by fixing carbon with their energy deriving from photosynthesis, carbon (C) and nitrogen (N) play the crucial role in regulating plant growth, community structure and function in the vegetation restoration progress. However, it is still unclear how habitat types affect the dynamic changes of allocation in C and N storage of vegetation-soil system in sandy grasslands. Here, we investigated plant community characteristics and soil properties across three successional stages of habitat types: semi-fixed dunes (SFD), fixed dunes (FD) and grasslands (G) in 2011, 2013 and 2015. We also examined the C and N concentrations of vegetation-soil system, and estimated their C and N storage. The C and N storage of vegetation system, soil and vegetation-soil system remarkably increased from SFD to G. The litter C and N storage in SFD, N storage of vegetation system in SFD and N storage of soil and vegetation-soil system in FD increased from 2011 to 2015, while aboveground plant C and N storage of FD were higher in 2011 than in 2013 and 2015. Most of C and N were sequestered in soil in the vegetation restoration progress. These results suggest that the dynamic changes of allocation in C and N storage in vegetation-soil systems varied with habitat types. Our study highlights that SFD has higher N sequestration rate in vegetation, while FD has the considerably N sequestration rate in the soil.</p>
The global decline in the sensitivity of vegetation productivity to precipitation from 2001-2018
<p><span>The sensitivity of vegetation productivity to precipitation (<em>S<sub>ppt</sub></em>) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of </span><span><em>S<sub>ppt</sub></em></span><span> over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of </span><span><em>S<sub>ppt</sub></em></span><span> across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of </span><span><em>S<sub>ppt</sub></em></span><span> with different satellite products: Sppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest </span><span><em>S<sub>ppt</sub></em></span><span>, and evergreen needle-leaf forest and wetland showed the lowest. Temporally, </span><span><em>S<sub>ppt</sub></em></span><span> showed a generally declining trend over the past two decades (<em>p</em> < 0.05), yet with clear spatial heterogeneities. The decline in </span><span><em>S<sub>ppt</sub></em></span><span> was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, </span><span><em>S<sub>ppt</sub></em></span><span> showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decreases in </span><span><em>S<sub>ppt</sub></em></span><span>, while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in </span><span><em>S<sub>ppt</sub></em></span><span>. Our finding of the overall decline in </span><span><em>S<sub>ppt</sub></em></span><span> implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed </span><span><em>S<sub>ppt</sub></em></span><span> increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.</span></p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 08 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 08 after drought stress application. </p>
FIGURE 1. Deamia funis, vegetative characters. A–C in A new species of Deamia (Cactaceae) from Nicaragua
FIGURE 1. Deamia funis, vegetative characters. A–C. Habitat and habit (Stevens & Montiel 33938). D. Close-up of stem showing cracks in epidermal wax (Stevens & Montiel 39242). Photo credits: all Montiel.
FIGURE 1. Serjania urvilleoides. A. Flowering branch. B. Vegetative branch. C. Stipules. D. Inflorescence. E. Immature fruits. F. Mitotic metaphase, 2 n in A new combination in Serjania (Sapindaceae, Paullinieae) endemic to Minas Gerais, Brazil
FIGURE 1. Serjania urvilleoides. A. Flowering branch. B. Vegetative branch. C. Stipules. D. Inflorescence. E. Immature fruits. F. Mitotic metaphase, 2 n = 24. Photos taken by J. D. Urdampilleta.
MUSES Normalized Difference Vegetation Index (NDVI) Monthly Global 0.05º Geographic Grid Since 1982
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global NDVI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1982 – 2015</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Normalized Difference Vegetation Index (NDVI) 8-Day Global 0.05º Geographic Grid Since 1982
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global NDVI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1982 – 2015</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2017
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2017. <em>Please <a href="https://zenodo.org/record/7830319#.ZDtHqXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2016</strong></em>, and <em><a href="https://zenodo.org/record/7496083#.Y7GWb9VBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Fig. 2 in Variation in diet of frugivorous bats in fragments of Brazil's Atlantic Forest associated with vegetation density
Fig. 2.—Carbon and nitrogen isotopic ratios for each bat population presented as mean and standard deviation. Each panel represents one species–season pairing: Al-H is Artibeus lituratus in the Humid season; Cp-H is Carollia perspicillata in the Humid season; Cp-S is C. perspicillata in the super-humid season; Sl-S is Sturnira lilium in the super-humid season. Fragments and population designations correspond to fragments in Brazil's Atlantic Forest in Fig. 1. Shades correspond to fragment where the sample was collected ordered by area. Darker greens are largest fragments; darkest purple are smallest fragments.
Fig. 1 in Variation in diet of frugivorous bats in fragments of Brazil's Atlantic Forest associated with vegetation density
Fig. 1.—Map of study sites situated in Brazil's Atlantic Forest, Rio de Janeiro State, Brazil. Sites in Reserva Ecologica de Guapiacu ("REGUA") and fragments (F) adjacent to this area are indicated as points. Thirteen areas were sampled and allocated as REGUA, REGUA2, REGUA3 for those sampled in the reserve (considered repeated efforts sampling in the same fragment), and 10 fragments designated as F1 through F10. This figure was constructed using ESRI base maps and existing maps available through Instituto Brasileiro de Geografia (IBGE) (SOS Mata Atlântica (2009) (www.sosma.org). Used under commons licence). Figure is adapted from Teixeira (2019).
Vegetation resistance and resilience to a decade-long dry period in the temperate grasslands in China
<p>The duration of climate anomalies has been increasing across the globe, leading to ecosystem function loss. Thus, we need to understand the responses of the ecosystem to long-term climate anomalies. It remains unclear how ecosystem resistance and resilience respond to long-term climate anomalies, e.g., continuous dry years at a regional scale. Taking the opportunity of a 13-year dry period in the temperate grasslands in northern China, we quantified the resistance and resilience of the grassland in response to this periodic dry period. We found vegetation resistance to the dry period increased with mean annual precipitation (MAP), while resilience increased at first until at MAP of 250 mm and then decreased slightly. No trade-off between resistance and resilience was detected when MAP < 250 mm. Our results highlight that xeric ecosystems are most vulnerable to the long-term dry period. Given expected increases in drought severity and duration in the coming decades, our findings may be helpful to identify vulnerable ecosystems in the world for the purpose of adaptation.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.