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AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Geomedian Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Geomedian Composites  The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

opencc-zeroJul 2022View details →

Disentangling the interrelated abiotic and biotic pathways linking landscape composition and crop production

Landscape composition and its related functional agrobiodiversity (FAB) was severely simplified during the last decades. As landscape composition is expected to influence the interrelated microclimate and arthropod community at different scales, this simplification might have led to a decline in multiple agro-ecosystem services, with potential impacts for the growth of crops with different demands from the environment. To study landscape-scale effects on multi-crop yield and herbivory in combination with its potential drivers, including functional arthropod community and microclimate, we used standardised 1 m² mini-gardens planted with ten different vegetable crops as phytometers. The gardens were installed at locations varying in surrounding landscape composition and the vegetables were selected to have contrasting requirements in terms of growing conditions, pollination services, and susceptibility to pests. Monitoring of the mini-gardens was performed in 2018 and 2019 in Flanders (Belgium). We found no relationship between landscape composition in a 500 meter radius and crop yield. Considering possible mechanisms, we found that an increasing share of woody vegetation (> 3 m high) in the surrounding landscape is important to buffer the temperature and soil moisture variation in the mini-gardens. The share of agricultural land use and urban green (< 3 m high) is, respectively, positively and negatively related with the activity-density of natural enemies and pollinators. The growth of different crops responded differently to higher temperature ranges and we found no relation between natural enemies and herbivory. In conclusion, these abiotic and biotic pathways did not cause an overall relationship between landscape composition and yield.

Llanddwyn Island chlorite compositions

Elemental compositions of chlorites from Gwna Complex, Llanddwyn Island, Anglesey, North Wales, derived from EDS (electron dispersive spectrometry) spectra. The data are reported, and their context described, in Leah et al., Heterogeneous subgreenschist deformation in an exhumed sediment-poor mélange, submitted to Journal of Geophysical Research.

Chemical composition of PM2.5 for Deyang campaign

The file contains the concentrations of different PM2.5 compositions measured by a ToF-ACSM, including organics, nitrate, sulphate, ammonium and chloride (μg/m3). The interval of the campaign was from 18 December 2021 to 22 January 2022, adn the temporal resolution of the data is 10 min.

opencc-zeroAug 2022View details →

Developmental changes in ciliary composition during gametogenesis in Chlamydomonas

Chlamydomonas reinhardtii transitions from mitotically dividing vegetative cells to sexually competent gametes of two distinct mating types following nutrient deprivation. Gametes of opposite mating type interact via their cilia, initiating an intraciliary signaling cascade and ultimately fuse forming diploid zygotes. The process of gametogenesis is genetically encoded and a previous study revealed numerous significant changes in mRNA abundance during this life cycle transition. Here we describe a proteomic analysis of cilia derived from vegetative and gametic cells of both mating types in an effort to assess the global changes that occur within the organelle during this process. We identify numerous membrane and/or matrix-associated proteins in gametic cilia that were not detected in cilia from vegetative cells. This includes the pro-protein from which the GATI-amide gametic chemotactic modulator derives, as well as receptors, a dynamin-related protein, ammonium transporters, two proteins potentially involved in the intraciliary signaling cascade-driven increase in cAMP, and multiple proteins with a variety of interaction domains. These changes in ciliary composition likely directly affect the functional properties of this organelle as the cell transitions between life cycle stages.

Molecular Level Estimation of the Composition and Fate of Dissolved Organic Matter in Fire-Impacted Temperate and Subtropical Peatlands

Calculated values of optical (UV-visible and fluorescence) spectroscopy parameters and molecular formulas determined by ultrahigh resolution mass spectrometry for dissolved organic matter (DOM) in porewaters from a temperate and sub-tropical peatland. These data were used to generate figures in a manuscript submitted to the Journal of Geophysical Research - Biogeosciences.

opencc-zeroAug 2022View details →

Dataset: Local hydrological conditions influence tree diversity and composition across the Amazon basin

Tree diversity and composition in Amazonia are known to be strongly determined by the water supplied by precipitation. Nevertheless, within the same climatic regime, water availability is modulated by local topography and soil characteristics (hereafter referred to as local hydrological conditions), varying from saturated and poorly drained to well-drained and potentially dry areas. While these conditions may be expected to influence species distribution, the impacts of local hydrological conditions on tree diversity and composition remain poorly understood at the whole Amazon basin scale. Using a dataset of 443 1-ha non-flooded forest plots distributed across the basin, we investigate how local hydrological conditions influence 1) tree alpha diversity, 2) the community-weighted wood density mean (CWM-wd) – a proxy for hydraulic resistance, and 3) tree species composition. We find that the effect of local hydrological conditions on tree diversity depends on climate, being more evident in wetter forests, where diversity increases towards locations with well-drained soils. CWM-wd increased toward better-drained soils in Southern and Western Amazonia. Tree species composition changed along local soil hydrological gradients in Central-Eastern, Western and Southern Amazonia, and those changes were correlated with changes in the mean wood density of plots. Our results suggest that local hydrological gradients filter species, influencing the diversity and composition of Amazonian forests. Overall, this study shows that the effect of local hydrological conditions is pervasive, extending overwide Amazonian regions, and reinforces the importance of accounting for local topography and hydrology to better understand the likely response and resilience of forests to increased frequency of extreme climate events and rising temperatures.

Meridional composite pulses for low-field magnetic resonance

We discuss procedures for error-tolerant spin control in environments that permit transient, large-angle reorientation of magnetic bias field. Short sequences of non-resonant magnetic field pulses in a laboratory-frame meridional plane are derived. These are shown to have band-pass excitation properties comparable to established amplitude-modulated, resonant pulses used in high, static-field magnetic resonance. Using these meridional pulses, we demonstrate robust z inversion in proton (1H) nuclear magnetic resonance near Earth’s field.

opencc-zeroSep 2022View details →

Relationship of woody species composition with edaphic characteristics in threatened riparian Atlantic Forest remnants in the upper Rio Doce basin, Brazil

Studies on the composition, richness, and diversity of plant species in tropical communities are essential for understanding relevant ecological processes and for developing appropriate conservation policies. Considering that areas subject to direct impacts due to dam breach may in the long-term present changes in species composition and in soil parameters, we evaluated the composition of the flora, described the current vegetation profile, and evaluated whether differences in species composition was influenced by soil variables of three areas along the Gualaxo River, in Minas Gerais State, Brazil. In addition, we identified important plant species through occurrence and phytosociological parameters for ecological restoration projects in the affected region, serving as reference areas. We sampled plant species with DBH ≥ 5 cm (diameter at breast height – measured 1.30 m above ground level) in 77 plots distributed in three riparian forest areas. We calculated phytosociological parameters and related them to edaphic factors. A total of 1579 individual plants belonging to 53 botanical families and 227 species were sampled in the three areas. The Fabaceae family was the most representative with 46 species. Species composition and diversity among the sampled areas was similar and was associated with edaphic factors. Furthermore, some species (e.g. Xylopia sericea, Cupania emarginata and Ocotea pulchalla) showed an important relation with soil variables. Some species of the genera (e.g., Byrsonima, Xylopia, Ocotea, and Croton) and families (e.g., Fabaceae and Myrtaceae) found here, can be important species in the restauration process for the local and regional maintenance of floristic identity in the Rio Doce river.

opencc-zeroSep 2022View details →

Chronic browsing by an introduced mammalian herbivore in a tropical island alters species composition and functional traits of forest understory plant communities

Mammalian herbivores have large-scale impacts on vegetation, altering structure and species composition, especially in tropical grasslands and savannas. However, there is limited understanding of the potential impacts of mammalian herbivores in tropical wet forests, where they are typically less abundant. We investigated the effects of an introduced mammalian herbivore chital (Axis axis) on vegetation structure, composition and leaf functional traits of tropical evergreen forests of the Andaman Islands, India. Across seven islands, representing a gradient of herbivore densities, increasing chital presence was associated with decreased understory richness, understory density and adult tree richness, but was not related to adult tree density or size class distributions. We also found a significant decrease in community-level leaf palatability traits (specific leaf area decreased and leaf thickness increased) with increasing chital habitat use. This community-level shift in leaf trait values was better explained by intra-specific variation in leaf traits across islands rather than changes in species composition. In summary, we show persistent long-term impacts of an introduced mammalian herbivore on understory tropical tree communities although there is little impact on adult tree communities. Our results also show that functional traits of species can be altered in response to novel herbivory, even at herbivore densities where there are no detectable impacts on adult forest structure or composition. Such altered functional traits may potentially alter ecosystem functioning in these forests even without changes in vegetation structure.

opencc-by-4.0Sep 2022View details →

Microbiome network connectivity and composition linked to disease resistance in strawberry plants

The two R data files correspond phyloseq objects used in the study. The file phyloseq_nochim_silva.RData and phyloseq_nochim_unite.RData correspond to bacterial and fungal phyloseq objects, respectively. Both objects include count table, taxonomy table and metadata information.

opencc-zeroSep 2022View details →

Data from: Nitrogen enrichment alters multiple dimensions of grassland functional stability via changing compositional stability

Anthropogenic nutrient enrichment is known to alter the composition and functioning of plant communities. However, how nutrient enrichment influences multiple dimensions of community- and ecosystem-level stability remains poorly understood. Using data from a nitrogen (N) and phosphorus (P) addition experiment in a temperate semi-arid grassland that experienced a natural drought, we show that N enrichment, not P enrichment, decreased grassland functional and compositional temporal stability, resistance, and recovery, but increased functional and compositional resilience. Compositional stability and species asynchrony, rather than species diversity, were identified as key determinants of all dimensions of grassland functional stability, except for recovery. Whereas grassland functional recovery was decoupled from compositional recovery, N enrichment altered other dimensions of functional stability primarily through changing their corresponding compositional stability dimensions. Our findings highlight the need to examine ecological stability at the community level for a more mechanistic understanding of ecosystem dynamics in the face of environmental change.

opencc-by-4.0Sep 2022View details →

Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator

This is the dataset for the research paper "<a href="https://arxiv.org/abs/2207.03239 ">Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator</a>"

opencc-by-4.0Oct 2022View details →

Dataset for "Definition and rationale for placebo composition: Cross-sectional analysis of randomized trials and protocols published in high-impact medical journals"

Contains our data extraction sheets

opencc-by-4.0Dec 2022View details →

Datasets for "Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations"

Files used to build Figures 3, 4, 5, 7, 9, 10, 13 in manuscript entitled "Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations" submitted with JGR.

Figs 1–3 in Effects of diet and feed composition on antibacterial activity of hemolymph of saproxylic beetles: A case study of Zophobas atratus (Coleoptera: Tenebrionidae)

Figs 1–3. Results of disk-diffusive test. Effects of artificial fungi-based and standard