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94 results for “vegetation dynamics”
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Forest stand structure in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes forest stand structure. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: GPS coordinates for a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes GPS coordinates and elevation data across a 75x75m spatial domain in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years
<p>Groundwater hydrology plays an important role in coastal marsh biogeochemical function, in part because groundwater dynamics drive the zonation of macrophyte community distribution. Changes that occur over time, such as sea level rise and shifts in habitat structure are likely altering groundwater dynamics and eco-hydrological zonation. We examined tidal flooding and marsh water table dynamics in 1999 and 2019 and mapped shifts in plant distributions over time, at Piermont Marsh, a brackish tidal marsh located along the Hudson River Estuary near New York City. We found evidence that the marsh surface was flooded more frequently in 2019 than in 1999, and that tides were propagating further into the marsh in 2019, although marsh surface elevation gains were largely matching that of sea level rise. The changes in groundwater hydrology that we observed are likely due to the high tide rising at a rate that is greater than that of mean sea level. In addition, we reported on changes in plant cover by <em>P. australis</em>, which has displaced native marsh vegetation at Piermont Marsh. Although <em>P. australis</em> has increased in cover, wrack deposition and plant die off associated Superstorm Sandy allowed for native vegetation to rebound in part of our focus area. These results suggest that climate change and plant community composition may interact to shape ecohydrologic zonation. Considering these results, we recommend that habitat models consider tidal range expansion and groundwater hydrology as metrics when predicting the impact of sea level rise on marsh resilience.</p>
Data for fitting a statistical global burned area model for seamless integration into Dynamic Global Vegetation Models
<p>The dataset is a large R data.table object saved in RDS format. It contains global, monthly data spanning the period from 2002 to 2018, with a 0.5 degrees spatial resolution. The dataset is utilized to develop and validate statistical models for predicting global burnt areas resulting from wildfires.</p>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
Figure 4 in Population Dynamics of Amoeboid Protists in a Tropical Desert: Seasonal Changes and Effects of Vegetation and Soil Conditions
Figure 4. Relationship between amoeboid protist richness and soil parameters during the wet season in three microhabitats by CCA: PL: Pr. laevigata, PP: Pa. praecox, and BS: bare soil. The names and abbreviations of the amoeboid protist species can be found in table 3.
Figure 2 in Population Dynamics of Amoeboid Protists in a Tropical Desert: Seasonal Changes and Effects of Vegetation and Soil Conditions
Figure 2. Cumulative richness plots of amoeboid protists present under Pr. laevigata (PL), Pa. praecox (PP) and bare soil (BS) during dry and wet seasons at 0–30 cm. a) eruptive pseudopods, and b) acanthopodial pseudopods. ND: not determined.
Figure 1 in Population Dynamics of Amoeboid Protists in a Tropical Desert: Seasonal Changes and Effects of Vegetation and Soil Conditions
Figure 1. Study area, showing vegetation patches in the desert of Tehuacán, Puebla, Mexico. In addition, the analyzed microhabitats are shown: Pr. laevigata, Pa. praecox and bare soil.
Рис. 2. Низина Λевобережья НТТ в виΑе параΛΛеΛьных берегу моря ваΛов и небоΛьших понижений, вытянутых в северо-восточном направΛении, с характерной раститеΛьностью Fig. 2. The lowland of the left Bank of the LRT in the form of parallel to the seashore shafts and small depressions, elongated in a North-Eastern direction, with characteristic vegetation in Transboundary Migration And The Local Constraints In The Dynamic Of Fish Fauna In The Lower Reaches Of Tumannaya River
Рис. 2. Низина Λевобережья НТТ в виΑе параΛΛеΛьных берегу моря ваΛов и небоΛьших понижений, вытянутых в северо-восточном направΛении, с характерной раститеΛьностью Fig. 2. The lowland of the left Bank of the LRT in the form of parallel to the seashore shafts and small depressions, elongated in a North-Eastern direction, with characteristic vegetation
Data from: Vegetation dynamics 1946–2018 in an old-growth conifer forest
<p>We analysed ground vegetation in 250 plots in 2018 in the old-growth <em>Picea abies-</em>dominated<em> </em>forest Säby Västerskog, SE Sweden as a follow-up of studies in 1946 and 1998 with the same layout of plots. The vegetation changes were not clearly directional: the species composition in 2018 was intermediate between that of 1946 and 1998, whereas in 1998 species richness was higher and with the unique presence of a number of species indicating small-scale disturbances. <em>Vaccinium myrtillus</em> increased in cover since 1946 and <em>Avenella flexuosa</em> decreased. This goes against regional trends, attributed to climate warming and changes in nitrogen deposition. Regional changes are overshadowed by fine-scale disturbances and micro-successions.</p>
Data from: Vegetation dynamics 1946–2018 in an old-growth conifer forest
Open the record for dataset details and reuse information.
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
Open the record for dataset details and reuse information.
Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years
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Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
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Wave Dynamics and Fluid Stresses in Vegetated and Unvegetated Coastal Lagoons in Virginia, 2010-2011
Wave, turbulence and wind measurements were performed within a Zostra marina seagrass (eelgrass) meadow in South Bay, Virginia, USA a coastal bay within the Virginia Coast Reserve where ongoing seagrass restoration efforts are being performed, and in adjacent areas without seagrass. These datasets were published in: Hansen J.C.R. and Reidenbach M.A., 2013, Seasonal growth and senescence of a Zostera marina seagrass meadow alters wave-dominated flow and sediment suspension within a coastal bay, Estuaries and Coasts, 36, 1099-1114.
Supplementary Files to "Towards a global understanding of vegetation-climate dynamics at multiple time scales"
<p><strong>TimeSeriesPaper_main_processing.html</strong></p> <p>Html version of Jupyter notebook exemplifying main data analysis steps in "Towards a global understanding of vegetation-climate dynamics at multiple time scales" (doi:10.5194/bg-17-945-2020). Please see the publication for further details. Notebook written in Julia v0.6</p> <p><strong>Fig2_Co_Oscillation_Regimes.nc</strong></p> <p>Data underlying Fig. 2 in "Towards a global understanding of vegetation-climate dynamics at multiple time scales" (doi:10.5194/bg-17-945-2020).</p> <p>Classification of land surface by dominant scale of variability in Normalized Difference Vegetation Index (NDVI), air temperature, and precipitation. Dominant scale of variability was determined after Fourier decomposition, separately for each grid cell and summarized as unique combinations between variables (S - short-term, A - seasonal, L - longer-term, T - trend, listed in order NDVI, air temperature, precipitation). The semi-annual cycle is included in the seasonal band. Please see the publication for further details.</p>
VTFT_Demography: global ageclass simulation data from the LPJ-wsl v2.0 Dynamic Global Vegetation Model
<p>Forest ecosystem processes follow classic responses with age, peaking production around canopy closure and declining thereafter. Although age dynamics might be more dominant in certain regions over others, demographic effects on net primary production (NPP) and heterotrophic respiration (Rh) are bound to exist. Yet, explicit representation of ecosystem demography is notably absent in most global ecosystem models. This is concerning because the global community relies on these models to regularly update our collective understanding of the global carbon cycle. This paper aims to fill this gap in understanding by presenting the technical developments of a computationally-efficient approach for representing age-class dynamics within a global ecosystem model, the LPJ-wsl v2.0 Dynamic Global Vegetation Model. The modeled age-classes are initially created by fire feedbacks, wood harvesting, and abandonment of managed land, otherwise aging naturally until a stand-clearing disturbance is simulated or prescribed. In this paper, we show that the age-module can capture classic demographic patterns in stem density and tree height compared to inventory data, and that patterns of ecosystem function follow classic responses with age. We also present a few scientific applications of the model to assess the modeled age-class distribution over time and to determine the demographic effect on ecosystem fluxes relative to climate. Simulations show that, between 1860 and 2016, zonal age distribution on Earth was driven predominately by fire, causing a ~45-year difference in ages between boreal (50N-90N) and tropical (23S-23N) latitudes. Land use change and land management was responsible for an additional decrease in zonal age by -6 years in boreal and by -21 years in temperate (23N-50N) and tropical latitudes, with the anthropogenic effect on zonal age distribution increasing over time. A statistical model helped reduced LPJ-wsl complexity by predicting per-grid-cell annual NPP and Rh fluxes by three terms: precipitation, temperature and age-class; at global scales, R<sup>2</sup> was between 0.95 and 0.98. As determined by the statistical model, the demographic effect on ecosystem function was often less than 0.10 kg C m<sup>-2</sup> yr<sup>-1</sup> but as high as 0.60 kg C m<sup>-2</sup> yr<sup>-1</sup> where the effect was greatest. In eastern forests of North America, the demographic effect was of similar magnitude, or greater than, the effects of climate; demographic effects were similarly important in large regions of every vegetated continent. Spatial datasets are provided for global ecosystem ages and the estimated coefficients for effects of precipitation, temperature and demography on ecosystem function. The discussion focuses on our finding of an increasing role of demography in the global carbon cycle, the effect of demography on relaxation times (resilience) following a disturbance event and its implications at global scales, and a finding of a 40-Pg C increase in turnover from age dynamics at global scales. Whereas time is the only mechanism that increases ecosystem age, any additional disturbance not explicitly modeled will decrease age. This LPJ-based age-module therefore simulates the upper limit of age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.</p>
Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
<p></p><p>Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS. </p><p></p>
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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.