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Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 00 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 02 and day 0 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 12 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 02 and day 12 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 10 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 02 and day 10 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 14 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 02 and day 14 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 02 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 02 and day 02 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 04 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 02 and day 04 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 02 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 03 and day 02 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 00 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 03 and day 00 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 06 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 03 and day 06 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 04 - raw image data

Cowpea (Vigna unguiculata) 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.  This dataset represents the images collected for Screen number 03 and day 04 after drought stress application.

opencc-zeroJan 2023View details →

Botanic records from the forest reserves of south west Ghana: Plant species distribution data with checklist and conservation assessments from 114 vegetation plots

South west Ghana is a biodiversity hotspot within the western African lowland tropical rainforest region, supporting many endemic and restricted range plant species. This dataset comprises botanic records from five forest reserves of south west Ghana (Ankasa, Boi Tano, Tano Nimri, Jema Assemkron, Nini Suhein). Vascular plant species distribution data (12,232 records) from 114 vegetation plot samples are presented, surveyed between 1981 and 2015. A plant species checklist including conservation assessments for each species is included. Nomenclature is current as of 2016. The dataset is linked to the publication Marshall et al, 2023, Implications for conservation assessment from flux in the botanical record over 20 years in south west Ghana, Ecology and Evolution <a href="https://doi.org/10.1002/ece3.9775">https://doi.org/10.1002/ece3.9775</a>. The dataset is also used in Marshall et al, 2022, Predictors of plant endemism in two west African forest hotspots, Frontiers in Ecology and Evolution 10:980660 <a href="https://doi.org/10.3389/fevo.2022.980660">https://doi.org/10.3389/fevo.2022.980660</a>.

opencc-zeroJan 2023View details →

Data from: A study on the Influence of submergence ratio on the transportation of suspended sediment in a partially vegetated channel flow

Riparian or aquatic vegetation thrives with seasons. The understanding of canopies' Submergence-Ratio SR (stems' height to water depth) influence on suspended sediments' transportation is still limited. Thus, Large Eddy Simulations (LES) coupled with the Discrete Phase Method (DPM) are used to investigate the particles' 3-dimensional distribution in a partially vegetated straight channel. The spanwise distribution of particles is quantified by the Probability Density Function (PDF), showing a non-uniformity of particles in time as quantified by the PDF variance. The findings and conclusions: (Ⅰ) With SR rising, the particles' depletion effects exerted by the vegetation-side mixing layer are improved along the interface between vegetated and vegetation-side bare channel region. However, the SR has little effect on the variance of the particles' PDF in the spanwise direction when the mixing layer is fully developed. (Ⅱ) During the developing stage of the over-canopy mixing layer, submerged vegetation with higher SR gain a stronger upwards (vertical) entrainment capability. The case (SR=60%) has a higher sediment concentration than other cases in the fully developed vertical mixing layer region above canopy. (III) The vertical suspension of particles in the vegetation-side bare channel region is analysed. Particles migrating from the vegetated region are entrained into the vegetation-side bare channel region by turbulent structures. Nevertheless, the vertical concentration profile is more uniform in the vegetated region than in the vegetation-side bare channel at the same streamwise location. The cases SR=40% and 60% still have higher sediment concentrations than other cases in the vegetation-side bare channel's upper region.

opencc-zeroFeb 2023View details →

Data for: Mammalian predators and vegetated nesting habitat drive reduced protected area nesting success of Kentish plovers, Yellow Sea region, China

Protected areas provide essential habitats for wildlife by conserving natural and semi-natural habitats and reducing human disturbance. However, whether breeding birds vulnerable to nest predation can benefit from strict land management in the protected area is unclear. Here, we compare the nesting performance of two groups of a ground-nesting shorebird, the Kentish plover (Charadrius alexandrinus), in the protected area (Liaohekou Natural Reserve, hereinafter PA) and the control non-protected area (Non-PA) around the Liaohekou Natural Reserve, in the north of the Yellow Sea, China, and identify which environmental factors, such as nesting habitat and nest materials, influence the daily nest survival rate (DSR). We found similar nesting habitats in both study areas, dominated by bare land or Suaeda salsa grassland. However, DSR was lower in PA (0.91 ± 0.01) than in Non-PA (0.97 ± 0.01). Kentish plovers nesting in areas with vegetation cover experienced lower DSR than in bare lands in both areas, and nests built with materials of S. salsa sticks had the lowest DSR in the bare land. Data from infrared cameras confirmed relatively higher predator abundances and nest predation rates by nocturnal mammals, such as Eurasian badgers (Meles meles), in PA than in Non-PA, and this pattern was especially evident for plover nests located in S. salsa grassland. Our results suggest that Liaohekou Natural Reserve protected area may not necessarily provide safe nesting sites for Kentish plovers due to the abundance of generalist mammal nest predators. However, the PA includes about 80% of the nests from both locations. This means the contribution of the total number of successful nests continues to be much higher within PA, with the benefit for the species that this brings in terms of conservation. The variation and mechanisms underlying differences in the nest predator communities of PA and non-PA deserve further study.

opencc-zeroFeb 2023View details →

Data for: The influence of vegetation structure on secondary diaspore dispersal by wind

The role of vegetation structure in relation to wind speed and diaspore attributes on secondary diaspore dispersal by wind has not been empirically studied. Here, we investigated secondary dispersal by wind of diaspores placed in 12 different kinds of vegetation and bare land. The experiments were conducted in a wind tunnel using a range of wind speeds and diaspores that differed in mass and kind of appendages. The explanations of wind speed, diaspore attributes, vegetation coverage, life-form, vertical pattern and horizontal pattern for diaspore dispersal capacity were 6.67~10.40%, 16.13~20.53%, 6.227~24.64%, 0.10%, 0.74%, and 0.10%, respectively. Compared with wind speed and diaspore attributes, vegetation coverage contributed the most to diaspore dispersal capacity when vegetation coverage was low (<10% in our study). However, but with a high (10-30%) coverage, vegetation coverage was the least influential factor in secondary diaspore dispersal by wind. Vegetation coverage significantly interacted with vegetation life-form, horizontal pattern and vertical pattern on affecting diaspore dispersal capacity. Thus, the most influential factor determining secondary diaspore dispersal by wind is vegetation coverage.

opencc-by-nc-4.0Oct 2022View details →

A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data

A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data. Introduction This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference: <blockquote> Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF </blockquote>   Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain Variables and files The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>: <ul> <li><code>planet-teff-4dmed-V4.0.zip</code> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><code>planet-teff-qf-4dmed-V4.0.zip</code> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><code>planet-vod-4dmed-V4.1.zip</code> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><code>planet-sm-4dmed-V4.0.zip</code> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><code>planet-sm-qf-4dmed-V4.0.zip</code> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><code>planet-sm-cor-4dmed-V4.0.zip</code> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><code>planet-aux-flags-4dmed-V4.0</code> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>). License The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>) <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> Contact Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a> Versions <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> Further information More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a> Background publications R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) Method and system for improving the resolution of sensor data, US10643098B2,EP3469516B1, WO2017216186A1 De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). A spatially coherent global soil moisture product with improved temporal resolution. Journal of hydrology, 516, 284-296. Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. The global long-term microwave vegetation optical depth climate archive (VODCA). Earth System Science Data, 12(1), pp.177-196. Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss. [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022 Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E, Remote Sensing of Environment, 189, pp.180-193. van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a>

opencc-zeroMar 2023View details →

Data from: Interplay between rainfall and hillslope hydrology determines drought resistance of tropical vegetation

Droughts are predicted to increase in both frequency and intensity by the end of the 21st century, but ecosystem response is not expected to be uniform. At the landscape scale, ecosystem response to drought is highly heterogeneous. Here we assess the importance of the hill-to-valley hydrologic gradient in shaping vegetation hydraulic properties related to drought resistance for three locations across a rainfall seasonality gradient in South America. For this, we use hydraulic traits related to xylem resistance to embolism and compare the functional composition and diversity of tree communities. We show that the hydrologic gradient systematically selects for community assemblages that are more vulnerable to embolism in valleys, regardless of rainfall. Under the same rainfall regime, diversity in resistance to embolism is higher on hills than valleys, suggesting that strategies to cope with drought are more important on hills. With increasing seasonality, diversity in embolism resistance increases on hills and decreases in valleys. Our results show that differential groundwater access from hilltops to valleys select for distinctive hydraulic properties, potentially explaining species turnover along topographical gradients. Incorporating this relationship might improve the representation of vegetation in climate models and the prediction of how different communities will respond to extreme droughts.

opencc-zeroMay 2023View details →

Data for: Vegetation and soil ecology of threatened Himalayan Trillium habitats in Kashmir, Himalaya

Understanding the vegetation and soil ecology of natural habitats harbouring threatened species is critical in conservation planning and restoration. The present study investigated the vegetation composition and soil physico-chemical attributes of natural habitats of Trillium govanianum – a threatened Himalayan endemic species. We laid 120 quadrats across eight randomly selected sites where the species was growing in the Kashmir Himalaya. We collected the soil samples from these sites and determined soil physico-chemical attributes using standard methods. Across all the sites, we found a total of 57 plant species with dominance of Rosaceae and Ranunculaceae. The IVI results revealed that Fragaria nubicola, Corydalis diphylla, Galium aparine, and Leucanthemum vulgare, were the dominant species in T. govanianum communities. The density, abundance and IVI of 3-leaf vegetative plants was higher than 1-leaf vegetative and 3-leaf reproductive plants across all the study sites. We found that T. govanianum alone forms 23.5 % positive, 0 negative, 76.4% random co-occurrences with other associated species in its community. Our results reveal that the variations in vegetation composition among the sites was influenced by differences in soil properties. Principal component analysis revealed that several soil parameters such as organic carbon, nitrogen, potassium, and sulphur were concentrated in five sites, namely Dara, Drung, Bangus, Gulmarg, and Doodhpathri, which also showed the highest density, frequency, and abundance of T. govanianum. Overall, our study contributes quantitative information on the vegetation and soil ecology of T. govanianum-assemblages, which in turn can help in developing conservation strategies for this threatened species, and its sustainable management and habitat restoration.

opencc-zeroJun 2023View details →

Data for: Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents

Aim: Theoretically, woody biomass turnover time (τ) quantified using outflux (i.e., tree mortality) predicts biomass dynamics better than using influx (i.e., productivity). This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody τ in mature forests. We further compared woody τ estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes - CARDAMOM. Location: Continents Time period: Historic from 1951 to 2018 Major taxa studied: Trees and Forests Methods: We compared the approaches of using outflux vs. influx for estimating woody τ and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody τ and generated a spatially explicit map of woody τ at a 0.25-degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody τ. Results: Woody τ quantified by the outflux approach better (with R2 0.4-0.5) predicted the biomass accumulation rates than the influx approach (with R2 0.1-0.4) across continents. We found large spatial variations of woody τ for mature forests, with highest values in temperate forests (98.8 ± 2.6 y) followed by boreal forests (73.9 ± 3.6 y) and tropical forests. The map of woody τ extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody τ across continents. The highest woody τ in temperate forests were not captured by either DGVMs or CARDAMOM. Main conclusions: Our study empirically demonstrated the preference of using outflux over influx to estimate woody τ for predicting biomass accumulation rates. The spatially explicit map of woody τ and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs.

opencc-by-4.0Jun 2023View details →

Supplementary files for "Data-driven estimation of nitric oxide emissions from global soils based on dominant vegetation covers"

In-situ observations collected from publications, data-driven model codes, DNDC simulation files, and the supporting data for all figures of this study are uploaded. In-situ observations included 1,356 observations of soil nitric oxide (NO) emissions from 192 sites, including 1,032 for cropland soils from 70 sites, 114 for grassland soils from 36 sites, and 208 for forest soils from 86 sites. Data-driven models provided three machine learning methods, including random forest (RF), generalized boosted regression model (GBM), and radial basis function (RBF). The DNDC simulation files included simulation files of 51 selected sites.