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openCC (other)Nov 2020View details →

Reactive oxygen species alter chemical composition and adsorptive fractionation of soil-derived organic matter

This is the data archive for the corresponding publication with the same title (doi: 10.1016/j.geoderma.2020.114805). Reactive oxygen species (ROS), formed during redox fluctuations in iron-rich soils, have been known to stimulate lignin degradation, although not much is known about how they alter soil organic matter (SOM) composition and interaction with mineral surfaces. We conducted a laboratory experiment to see how ROS altered SOM composition and adsorptive fractionation onto Fe-mineral surfaces. We reacted water extracts of SOM with ·OH, produced by the Fenton reaction, and then conducted a sorption experiment of the extracts with goethite to analyze the amount and quality of SOM adsorbed. The Fenton reaction preferentially consumed low-O, mostly aromatic molecules, and new high-O molecules were detected post-Fenton, nearly half of which were carbohydrate-like. Although the amount of C adsorbed did not change after oxidation, the post-Fenton adsorbed molecules were more oxidized. Pre-Fenton adsorption was dominated by aromatic molecules (90%), but post-Fenton, the adsorbed molecules were 75% aromatic and 25% carbohydrate-like. We show that the ·OH radical oxidized SOM and shifted patterns of adsorption to a more oxidized pool. Because adsorption to minerals is thought to stabilize SOM, our results suggest that ROS may alter the availability and stabilization patterns of SOM

openOpenJan 2020View details →

Vertebrate species composition of remnant desert islands within urban Phoenix: site locations.

These data represent the study site locations used to assess vertebrate species composition in central Arizona, Phoenix. Data include the transect Locations.

openOpenJan 2020View details →

Vertebrate species composition of remnant desert islands within central Arizona-Phoenix urban areas: period 1998-1999

Urban growth around Phoenix over the last 30 years has produced numerous isolated "islands" of remnant desert habitat. Each of these remnants has its own unique vertebrate community, determined by the fauna present prior to isolation, area of remaining habitat, vegetation characteristics, time since isolation, and distance to other islands or source populations. These factors can be used as predictors of how future growth will affect potential desert preserves.

openOpenNov 2016View details →

Linking foraging decisions to residential yard bird composition

Urban bird communities have higher densities but lower diversity compared with wildlands. However, recent studies show that residential urban yards with native plantings have higher native bird diversity compared with yards with exotic vegetation. Here we tested whether landscape designs also affect bird foraging behavior. We measured the amount of resources remaining, or giving-up densities (GUD) for final foragers on artificial food patches in residential yards in Phoenix, AZ, USA. We assessed how two yard designs (mesic: lush, exotic vegetation; xeric: drought-tolerant and native vegetation) differed in foraging costs. Further, we developed a statistical model to calculate GUDs for every species visiting the patch. Birds foraging in mesic yards depleted patches to a lower level (i.e. had lower GUDs) than birds foraging in xeric yards. After accounting for bird densities, the lower GUDs in mesic yards appeared largely driven by invasive and synanthropic species. Furthermore, yard design affected individual species behavioral responses. Species visiting trays in both yard designs had lower GUDs in mesic yards. Differences in the missed opportunity (alternative resources more abundant in xeric yards) and the digestive cost (moist conditions in mesic yards) both contributed to our results while predation costs did not. Enhancing the GUD method with our statistical model provided insights into how individual species and bird densities influenced the GUD. If the described differences we found in foraging behavior are indicative of differences in habitat quality, then this would lend additional support for native landscapes to help reverse the loss of urban bird diversity.

openCC0Jun 2021View details →

Multispectral Radiometry percent reflectance:The influence of natural enemies on plant community composition and productivity

The purpose of this experiment is to determine the influences of natural enemies, including plant pathogenic fungi and insect pests, influence plant community composition, productivity, and diversity over time. The experiment is being conducted in an old field that is burned every other year. Within the old field, there are 8 blocks, and within each block there are 6 treatments: foliar fungicide, soil drench fungicide, foliar insecticide, mammal exclosure, the combination of all enemy suppression tactics (pesticides and mammal exclosure), and a nontreated control. The pesticides are applied repeatedly throughout the growing season. Within the plots, community productivity, species composition, percent cover, and pest damage are being quantified over time.

openCC0Mar 2018View details →

Root biochemistry:Microbial composition and function across an old-field chronosequence

As mediators of biogeochemical cycles, understanding the ecological forces structuring soil microbial communities is of ecosystem-level significance. Due to gradual shifts in plant species composition and litter addition through time, succession can be used as a model to understand how plant communities shape microbial community composition and function in soil. Numerous studies have investigated microbial biomass and diversity along successional gradients, yet few have quantified changes in microbial communities. Using the established successional dynamics experiment at Cedar Creek, principal investigators Lauren C. Cline and Donald R. Zak investigated the influence of plant community composition in structuring microbial community composition and function. Specifically, their research addressed the following questions: 1. Do shifts in saprotrophic microbial communities correlate to changes in plant community composition through successional time? 2. What is the relative influence of soil properties and plant community characteristics in determining microbial community dynamics? Cline and Zak sampled soils from 8 established abandoned agricultural fields (e054), as well as three adjacent forests representing potential late-successional ecosystems, to investigate microbial dynamics using three complementary approaches: targeted sequencing of fungal and bacterial communities, quantitative PCR, and shotgun metagenomics. Further, the characterization of soil properties across the chronosequence will enable us to disentangle the impact of abiotic factors in structuring microbial communities.

openCC0Mar 2018View details →

Soil organic matter, total nitrogen and pH:Microbial composition and function across an old-field chronosequence

openCustomMar 2017View details →

Sediment trap results from Amundsen Sea Polynya International Research Expedition (ASPIRE) including C, N and mass fluxes and isotopic composition, Dec 2010 - Dec 2011

The gravitational settling of particles is a major biogeochemical process linking the carbon cycles and ecosystems of the upper ocean, twilight zone and deep sea. Particle interceptor or sediment traps are used to collect settling particles and provide estimates of their vertical fluxes to various depths. We used a new Technicap 24-sample time series sediment trap deployed at 350 m depth in the Amundsen Sea polynya (73o 49’ S, 113o 04’ W, 2010-12.

openCustomJun 2020View details →

Elemental composition, respiration, and excretion measurements of pteropods collected from the PAL LTER research cruises along the Western Antarctic Peninsula 2017-2019.

Pteropods (pelagic snails) are abundant zooplankton in the Southern Ocean where they are important grazers of phytoplankton, prey for higher trophic levels, and sensitive to environmental change. In this study, the effects of warming seawater temperatures and shifting food availability on Limacina helicina antarctica metabolism (respiration and excretion) were determined by conducting shipboard experiments that exposed pteropods to a range of temperatures and phytoplankton (food) concentrations. Pteropods and all other macrozooplankton were collected with a 2 m square frame Metro net (700 µm mesh), towed obliquely to depths up to 120 m (Ross et al., 2008; Steinberg et al., 2015), or with shallower (0-50 m), vertical tows if pteropods were very abundant. Pteropods were exposed to varying temperature and food conditions for 12 hours and respiration and excretion were measured. Incubations were also conducted with non-shelled pteropod species and shelled pteropod, Clio pyramidata, measuring pteropod respiration under in situ conditions. Finally, a CO2 perturbation experiment measuring L. h. antarctica metabolism (respiration and excretion) under pre-industrial and elevated dissolved CO2 conditions at ambient temperature in filtered seawater (no food), during the 2018 PAL LTER research cruise. Results reveal the metabolic response of pteropods to ocean variability, increase our understanding of the role of zooplankton in biogeochemical cycles, and help predict future responses to climate change.

openOpenMar 2012View details →

Kruger Species Composition: Konza-Kruger Fire-Grazing Project (2006-2010)

The distribution, structure and function of mesic savanna grasslands are strongly driven by fire regimes, grazing by large herbivores, and their interactions. This research addresses a general question about our understanding of savanna grasslands globally: Is our knowledge of fire and grazing sufficiently general to enable us to make accurate predictions of how these ecosystems will respond to changes in these drivers over time? Some evidence suggests that fire and grazing influence savanna grassland structure and function differently in South Africa (SA) compared to North America (NA). These differences have been attributed to the contingent factors of greater biome age, longer evolutionary history with fire and grazing, reduced soil fertility, and greater diversity of plants and large herbivores in SA. An alternative hypothesis is that differences in methods and approaches used to study these systems have led to differing perspectives on the role of these drivers. If the impacts of shared ecosystem drivers truly differ between NA and SA, this calls into question the generality of our understanding of these ecosystems and our ability to forecast how changes in key drivers will affect savanna grasslands globally. Since 2006, an explicitly comparative research program has been conducted to determine the degree of convergence in ecosystem (productivity, N and C cycling) and plant community (composition, diversity, dynamics) responses to fire and grazing in SA and NA. Thus far, initial support has been found for convergence at the ecosystem level and divergence at the community level in response to alterations in both fire regimes and grazing. However, there have also been two unexpected findings (1) the ways in which fire and grazing interact differed between NA and SA, and (2) the rate of change in communities when grazers were removed was much greater in NA than in SA. These unexpected findings raise a number of important new questions: (Q1) Will exclusion of grazi

openOpenMar 2012View details →

Konza Species Composition: Konza-Kruger Fire-Grazing Project (2006-2010)

RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.3

This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data. The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the "Data Access" tab above. It is advised for users to avoid using the "wind_obj" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the "ambiguity_obj" variable. The "wind_obj" variable contains DIRTH probabilities (which are derived form the "ambiguity_obj" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.

RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.1

This dataset contains the RapidScat Level 2B 12.5km Version 1.1 science-quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the "Data Access" tab above. This Version 1.1 dataset differs from the previous Version 1 dataset as follows: 1) A new neural network approach for high wind speeds provided rain corrections for the "retrieve_wind_speed" variable for wind speeds in excess of 15 m/s. 2) The data variables containing the number of measurements of each type for each wind vector cell have been corrected; these variables include "number_in_aft", "number_in_fore", "number_out_aft", and "number_out_fore". 3) The "wind_obj" data variable has been corrected to include the proper data for the conditional probability for the objective DIRTH function values. It is advised for users to avoid using the "wind_obj" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the "ambiguity_obj" variable. The "wind_obj" variable contains DIRTH probabilities (which are derived form the "ambiguity_obj" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions, please contact podaac@podaac.jpl.nasa.gov

RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.2

This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the "Data Access" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the "wind_obj" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the "ambiguity_obj" variable. The "wind_obj" variable contains DIRTH probabilities (which are derived form the "ambiguity_obj" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.

QuikSCAT Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 4.1

This dataset contains the latest reprocessed version 4.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is an official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). Version 4.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 4.0 processing. This new version has a number of key improvements and changes over the previous version 4.0, including: 1) winds are now retrieved to within 5-km and 10-km of the coast within oceans/seas and lakes respectively; 2) coastal winds are now flagged as poor coastal quality and likely corrupted in orbits with estimated spacecraft pitch error greater than 0.04 degrees, which affects 150 orbits of data where coastal winds are severely contaminated by land due to poor attitude knowledge (note: attitude error tracking can identify pitch error but not yaw error, so when estimated pitch error is far from zero, it implies yaw error is large and uncorrected); 3) coastal winds are flagged based upon the long term mean wind speed and standard deviation of wind speed for each place on the ground; 4) four quantities, means and standard deviations computed with and without the land contamination correction algorithm applied (note: higher mean and smaller standard deviation are evidence of land contamination), are used to estimate the expected wind speed bias with respect to neighboring wind vector cells over open water; 5) wind vector cells with estimated speed bias greater than 0.4 m/s are flagged as poor coastal quality and likely corrupted; 6) winds within 5-km of the coast of an ocean/sea and 10-km of the coast of a lake are flagged as poor coastal quality and likely corrupted; the larger distance threshold for lakes is due to higher variability in lake water levels.

Figure 9 from: Prathapan KD, Poorani J, Amritha Kumari S, Anuradha C, Padmanaban B, Thanigairaj R (2019) Species composition and diagnoses of leaf- and fruit-scarring beetles (Coleoptera, Chrysomelidae) infesting bananas and plantains (Zingiberales, Musaceae) in the Indian subcontinent. Deutsche Entomologische Zeitschrift 66(2): 179-202. https://doi.org/10.3897/dez.66.47447

Figure 9 Basilepta viridipennis (Jacoby). a, b. Syntype of Nodostoma occipitale Jacoby, a synonym of B. viridipennis (BMNH); c, d. Colour forms of B. viridipennis.

Figure 6 from: Prathapan KD, Poorani J, Amritha Kumari S, Anuradha C, Padmanaban B, Thanigairaj R (2019) Species composition and diagnoses of leaf- and fruit-scarring beetles (Coleoptera, Chrysomelidae) infesting bananas and plantains (Zingiberales, Musaceae) in the Indian subcontinent. Deutsche Entomologische Zeitschrift 66(2): 179-202. https://doi.org/10.3897/dez.66.47447

Figure 6 Female genitalia of Basilepta subcostata (Jacoby). a, b. Spermatheca; c. Bursa copulatrix; d. Ovipositor; e. Eighth sternite and tignum; f. Eighth sternite and 8th ventrite along with tignum; g. Female genitalia. Abbreviations: SpG = spermathecal gland; SpC = spermathecal capsule; SpD = spermathecal duct; MdO = median oviduct; BC = Bursa copulatrix; BS = bursa sclerite; Vg = vagina; CoG = collateral gland; Tg = tignum; ovi = ovipositor.

Figure 5 from: Prathapan KD, Poorani J, Amritha Kumari S, Anuradha C, Padmanaban B, Thanigairaj R (2019) Species composition and diagnoses of leaf- and fruit-scarring beetles (Coleoptera, Chrysomelidae) infesting bananas and plantains (Zingiberales, Musaceae) in the Indian subcontinent. Deutsche Entomologische Zeitschrift 66(2): 179-202. https://doi.org/10.3897/dez.66.47447

Figure 5 Endophallus of Basilepta subcostata (Jacoby). a. Endophallus completely everted, lateral view; b. Basal phallomere and central sclerite, dorsal view; c. Median phallomere, lateral view; d. Apical phallomere, dorsolateral view. Abbreviations: BP = Basal phallomere; MP = Median Phallomere; AP = Apical phallomere; CS = Central Sclerite.

Figure 4 from: Prathapan KD, Poorani J, Amritha Kumari S, Anuradha C, Padmanaban B, Thanigairaj R (2019) Species composition and diagnoses of leaf- and fruit-scarring beetles (Coleoptera, Chrysomelidae) infesting bananas and plantains (Zingiberales, Musaceae) in the Indian subcontinent. Deutsche Entomologische Zeitschrift 66(2): 179-202. https://doi.org/10.3897/dez.66.47447

Figure 4 Male genitalia of different populations of Basilepta subcostata (Jacoby). a, c, e, h, k, m–o. Aedeagus, lateral view; b, d, f, g, i, j, l. Apex of aedeagus, ventral view; p. Male: tergite VIII, sternite VIII and IX; q. Male genitalia, tegmen.

Figure 3 from: Prathapan KD, Poorani J, Amritha Kumari S, Anuradha C, Padmanaban B, Thanigairaj R (2019) Species composition and diagnoses of leaf- and fruit-scarring beetles (Coleoptera, Chrysomelidae) infesting bananas and plantains (Zingiberales, Musaceae) in the Indian subcontinent. Deutsche Entomologische Zeitschrift 66(2): 179-202. https://doi.org/10.3897/dez.66.47447

Figure 3 Basilepta subcostata (Jacoby). a. Head, dorsal view, male; b. Head, dorsal view, female; c. Pronotum, male; d. Pronotum, female; e. Middle leg; f. Hind leg.