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112 results for “pellet”
Litterfall and Hare Pellet Summary at Bonanza Creek LTER Control Plots (1985 - Present)
Litterfall weights and Hare Pellet counts at LTER Control Plots within the Bonanza Creek Experimental Forest and the Caribou Poker Creek Research Watershed.
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Quality assessment of biomass pellets available on the market: Example from Poland
<p><strong>Submitted data was used to write an article</strong>: Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., Quality assessment of biomass pellets available on the market: Example from Poland. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-024-33452-1</p> <p> </p> <p><strong>Funding acknowledgments</strong>: The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract</strong>: This study evaluates the quality of 30 biomass pellets sold for residential use in Poland. It provides data on their physical, chemical, and petrographic properties and compares them to existing standards and the information provided by the fuel producers. The results reveal considerable variations in the quality of the pellets and show that some of the purchased samples are not within the DINplus and/or ENplus certification thresholds. Among all 30 purchased samples, only one passes the quality thresholds set by the PL-US BIO, a newly established quality certification in Poland that combines quality assessment following DINplus with optical microscopy analysis. The primary issues causing a decrease in pellet quality include elevated ash and fines content, compromised mechanical durability, too low ash melting temperature, and additions of undesired additions like bark, inorganic matter, and petroleum products. Our research highlights the need for improved fuel quality control measures, and transparent and accurate product labeling, as well as the need for a comprehensive and publicly available national database of solid biomass fuel producers and fuels sold. These are essential steps toward increasing customers’ awareness and trust, encouraging them to embrace biomass fuels as reliable and sustainable sources of energy.</p> <p> </p>
The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland – in preparation.</p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract:<br></strong></p> <p><span>In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle. </span></p> <p><span>This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), ammonia (NH<sub>3</sub>), chlorine (Cl<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO), all surpassing recommended limits.</span></p> <p><span>Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible. </span></p> <p><strong> </strong></p>
Pellet-based fused deposition modeling for the development of soft compliant robotic grippers with integrated
<p>Fused deposition modeling (FDM) has some advantages compared to other additive manufacturing techniques, such as the in situ integration of functional components, like sensors, and recyclability of parts. However, conventional filament-based FDM techniques are limited to thermoplastic elastomers with a Shore hardness above 70 A, thus it has marginal compatibility with soft robotic structures. Due to recently emerging pellet-based FDM printer technology, the fabrication of soft grippers with low Shore hardness has become possible. In this study, styrene based thermoplastic elastomers (TPS) were used to print elastic strips and soft gripper structures down to a Shore hardness of 25 A with an integrated strain sensing element (piezoresistive sensor). Printing on a soft rather than rigid substrate affects the integration of the printed thread on the substrate, because of the softness and relaxation, during the printing softness. It was seen that integrating the sensing element on a substrate with higher Shore hardness decreased the elongation at the point of fracture and the sensitivity of the sensing element. A soft compliant gripper structure with an integrated sensing layer was printed with the TPS-based elastomers successfully, and even due to the complex deformation of the compliant gripper structure, several positions could be detected successfully. Opened and closed position of the gripper, as well as, size recognition of spools of different sizes could be monitored by the piezoresistive printed sensor layer. The most sensitive sensing performance was obtained with the TPS of the lower Shore hardness (25 A), as the value of relative change in resistance was 1, followed by the gripper of Shore hardness 65 A and a relative change in resistance of 0.51. With this study, we demonstrated that pellet-based FDM printers can be used, to print potential soft robotic structures with in-situ integrated sensor structures.</p>
Snowshoe hare pellet count data in Bonanza Creek Experimental Forest
Snowshoe hares, Lepus americanus, are a 'keystone' prey species in northern boreal forests and experience population fluctuations of 8-11-years. Despite intense responses of both vegetation and predators to changes in hare densities, landscape-scale comparisons of hare populations in Alaska have been limited to qualitative descriptions. We conducted capture-recapture studies of snowshoe hares at 5 locales in the Tanana valley, from Tok in the east to Clear in the west from 1999 to 2002. Snowshoe hare densities were highest in 1999 ( =6.36 ha-1, SE=0.63) and declined thereafter. We were unable to detect declines in apparent survival during declining densities in our study populations. Movement distances did not vary temporally and persistence of individuals through declining densities may be associated positively with body condition at the peak. The relationship of hare pellets and hare densities was weak and limits the utility of this methodology for estimating hare densities in Interior Alaska.
Sediment trap fecal pellets enumerations collected aboard CCE LTER process cruises in the California Current system, 2007, 2008 and 2016.
The collection and enumeration of sinking fecal pellets on CCE LTER Process cruises has been led by Mike Stukel since 2007. Sinking particles are collected in VERTEX-style particle interceptor traps (PIT) with an 8:1 aspect ratio, 70-mm diameter, and a baffle on top comprised of 13 smaller beveled tubes with a similar 8:1 aspect ratio. Tubes are deployed with a formalin-brine for a duration of 2-5 days. After recovery, samples are gently split on a Folsom splitter and typically 3/8 to 1/2 of two separate tubes are utilized for fecal pellet enumeration. After the cruise, samples for fecal pellet enumeration are placed in a settling chamber to allow fecal pellets to settle out. Overlying water is then strained through a 60-um filter to collect any pellets that may have remained in the water. Pellets were then placed on a gridded Petri dish and analyzed using a Zeiss Discovery stereomicroscope. Pellets were separated from other particles and photographed with a dedicated camera. Image processing was then conducted using either Image J or Image Pro to extract area and maximum feret length for each fecal pellet. Pellets were classified by shape and shape-appropriate equations were used to determine the volume of each fecal pellet. Volume was converted to mass using the equations in Stukel et al. (2013). ‘Sample’ refers to which of two samples the fecal pellet was contained within. ‘PelletID’ is the identifier for each fecal pellet in a sample. ‘Conversion Factor’ accounts for the proportion of a sample that was sorted for fecal pellets, as well as the deployment duration and cross-sectional area of the sediment trap. To determine the mass flux of fecal pellets of a certain type: 1) Sum the pellet mass for all fecal pellets of that type in a given sample and 2) multiply by the Conversion factor for that sample. ‘Shape’ is an identifier for the shape of a fecal pellet: 1) ovoid, 2) cylindrical, 3) spherical, 4) tabular, 5) amorphous, 6) ellipsoidal, 7) degraded fecal mater
Video S3 - Organic Pellet Application to Carrington Island Spawning Site
<p><strong>Video S3.</strong> Suppression of invasive lake trout by treatment of spawning sites with organic pellets to kill embryos in an IPM approach. Because only a few weeks are available to safely work on Yellowstone Lake following the peak of lake trout spawning each autumn, we expanded the embryo suppression research to include a comprehensive treatment of a spawning site with organic pellets by helicopter (with long line and seeder/spreader) to better understand the logistical constraints that may be faced when attempting large-scale, multi-site applications in the future. Dr. Christopher Guy of the USGS Montana Cooperative Fishery Research Unit describes the Carrington Island spawning site. During an October 2019 experimental treatment, all of the rocky substrate at this spawning site (0.5 ha) was treated with 18,000 kg of organic (soy and wheat gluten) pellets in less than one day. The pellets induce organic decomposition and decline in dissolved oxygen concentration, which is lethal to lake trout embryos, curtailing recruitment from the site. Relative to the expansive lake areas intensively gillnetted over a 22-week season (> 60 km of gill nets set daily), lake trout embryo suppression targets relatively small sites during a period of 2–3 weeks in autumn where the majority of a future year class is concentrated. Broad-scale application of pellets in autumn may reduce lake trout recruitment and enhance population suppression as part of an IPM approach targeting multiple lake trout life stages because the area of the 14 verified spawning sites is only 11.4 ha (0.03% of lake surface area).</p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p>Description for Zenodo Data Set DOI:10.5281/zenodo.45507</p> <p>This dataset accompanies the article, submitted to Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br /> (see https://www.hdfgroup.org/HDF5/) <br /> as HDF4 datafiles<br /> (see https://www.hdfgroup.org/release4/doc/index.html)<br /> and as CSV datafiles <br /> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5, 6</p> <p>---------------------------------------------<br /> start of figure list<br /> ---------------------------------------------<br /> FIGURE 1: No data set</p> <p>---------------------------------------------<br /> FIGURE 2: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br /> S140529016_ECRHData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000<br /> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 80 micro-sec<br /> samples: 25,000 <br /> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br /> FIGURE 3(a): (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br /> FIGURE 4 (a), (b), (c): (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Line-Density-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(nl-1)^2 dimensionless<br /> d(nl-2)^2 dimensionless <br /> d(nl-3)^2 dimensionless<br /> d(nl-4)^2 dimensionless<br /> Kappa 1-2 dimensionless<br /> Kappa 1-3 dimensionless<br /> Kappa 1-4 dimensionless</p> <p>Isat-Coherence.csv <br /> Frequency (Hz) Hz<br /> d(I)^2 dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>Float-Potential-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(Pot)^2/Te^ dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>---------------------------------------------<br /> FIGURE 5 (a), (b): (CSV Files)</p> <p>Figure 5(a)<br /> time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-CrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree</p> <p>Figure 5(b)<br /> time period: 6.02 - 6.05 sec<br /> Ensemble Window: 1.6 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-DuringCrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree<br /> kappa-Float dimensionless<br /> kappa-Isat dimensionless<br /> ---------------------------------------------<br /> FIGURE 6: No data set</p> <p>---------------------------------------------<br /> FIGURE 7: No data set</p> <p>---------------------------------------------<br /> FIGURE 8: No data set</p> <p>---------------------------------------------<br /> FIGURE 9: No data set</p> <p>---------------------------------------------<br /> FIGURE 10: No data set</p> <p>---------------------------------------------<br /> end of figure list<br /> ---------------------------------------------<br /> ---------------------------------------------<br /> start of file list<br /> ---------------------------------------------<br /> Filename Size <br /> ----------------------------------------------<br /> Potential-Time-Angle-Data.h5 254.68 KB <br /> All-Probe-Data.h5 9.81 MB <br /> Average-Probe-Data.h5 1.06 MB <br /> Average-Isat-Data.h5 422.22 KB <br /> S140529016_PDAData.hdf 6.80 MB <br /> S140529016_DensityData.hdf 6.00 MB <br /> S140529016_ECRHData.hdf 803.39 KB <br /> S140529016_LoopVoltage.hdf 203.39 KB <br /> Line-Density-Coherence.csv 420.59 KB <br /> Isat-Coherence.csv 258.51 KB <br /> Float-Potential-Coherence.csv 257.90 KB<br /> Float-Isat-DuringCrossPhase.csv 48.87 KB <br /> Float-Isat-CrossPhase.csv 138.47 KB <br /> LDX-Pellet-Supplementary.pdf 1.84 MB <br /> S140529016_frPlots.mp4 3.24 MB <br /> ---------------------------------------------<br /> end of file list<br /> ---------------------------------------------</p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p><strong>Description for Zenodo Data Set DOI:10.5281/zenodo.220992</strong></p> <p>This dataset accompanies the article, to appear in Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br> (see https://www.hdfgroup.org/HDF5/) <br> as HDF4 datafiles<br> (see https://www.hdfgroup.org/release4/doc/index.html)<br> and as CSV datafiles <br> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5</p> <p>Data plotted in other figures are derived from the dataset as described<br> in the paper.</p> <p>---------------------------------------------<br> start of figure list<br> ---------------------------------------------<br> <strong>FIGURE 1: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 2:</strong> (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br> S140529016_ECRHData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000<br> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 80 micro-sec<br> samples: 25,000 <br> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br> <strong>FIGURE 3(a)</strong>: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br> <strong>FIGURE 4 (a), (b), (c), (d), (e), (f): </strong> (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig4a-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(b) Fig4b-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(c) Fig4c-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(d) Fig4d-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(e) Fig4e-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(f) Fig4f-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>---------------------------------------------<br> <strong>FIGURE 5 (a), (b):</strong> (CSV Files)</p> <p>Figure 5(a)<br> time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig5a-Float-Isat-CrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree</p> <p>Figure 5(b)<br> time period: 6.02 - 6.05 sec<br> Ensemble Window: 1.6 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(b) Fig5b-Float-Isat-DuringCrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree<br> kappa-Float dimensionless<br> kappa-Isat dimensionless<br> ---------------------------------------------<br> <strong>FIGURE 6: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 7: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 8:</strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 9: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 10:</strong> No data set</p> <p>---------------------------------------------<br> end of figure list<br> ---------------------------------------------<br> ---------------------------------------------<br> <strong>start of file list</strong><br> ---------------------------------------------<br> Filename Size <br> ----------------------------------------------<br> All-Probe-Data.h5 9.8 MB<br> Average-Isat-Data.h5 422 KB<br> Average-Probe-Data.h5 1.1 MB<br> Fig4a-Line-Density-Coherence.csv 413 KB<br> Fig4b-Isat-Coherence.csv 251 KB<br> Fig4c-Float-Potential-Coherence.csv 250 KB<br> Fig4d-Line-Density-Coherence.csv 103 KB<br> Fig4e-Isat-Coherence.csv 62 KB<br> Fig4f-Float-Potential-Coherence.csv 62 KB<br> Fig5a-Float-Isat-CrossPhase.csv 138 KB<br> Fig5b-Float-Isat-DuringCrossPhase.csv 36 KB<br> Potential-Time-Angle-Data.h5 255 KB<br> S140529016_DensityData.hdf 6 MB<br> S140529016_ECRHData.hdf 803 KB<br> S140529016_LoopVoltage.hdf 203 KB<br> S140529016_PDAData.hdf 6.8 MB</p> <p><br> LDX-Pellet-Supplementary.pdf 1.8 MB<br> S140529016_frPlots.mp4 3.2 MB<br> ---------------------------------------------<br> <strong>end of file list</strong><br> ---------------------------------------------</p> <p> </p> <p> </p>
Figure 1. Experimental units for feeding rates tests with terrestrial isopods and fecal pellets from different food sources. A in Coprophagy in detritivores: methodological design for feeding studies in terrestrial isopods (Crustacea, Isopoda, Oniscidea)
Figure 1. Experimental units for feeding rates tests with terrestrial isopods and fecal pellets from different food sources. A) Treatment access; coprophagy is allowed. B) Treatment removal; coprophagy and bacterial activity on feces are avoided. C) Treatment net; coprophagy is avoided and bacterial activity on feces allowed. D) Fecal pellet from carrot (left) and decomposing leaf (right) consumption.
FIGURE 7 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 7. Feeding strategies and progressive growth of the Dotilla pellet structures. Note four types of feeding modes: sector (of a circle) feeding mode covering growth (top to bottom) of the pellet structures arranged in four columns (1-4) corresponding to four different types of pellet designs (homogeneous pellet spread, radial, concentric and concentric-radial); radially diverging feeding mode covering growth (top to bottom) of the radial and asteroid pellet designs (column 5); concentric feeding mode covering growth (top to bottom) of the concentric pellet designs (column 6) and combined concentric-radial feeding mode covering growth (top to bottom) of the concentric-radial pellet design (column 7). Note development of different designs under sector (of a circle) feeding mode within feeding sectors having similar shape and size (column 1- 4 top structures). Also note a pellet design may originate in different feeding modes, but with subtle differences. The lower half of the figure incorporates schematic representation of the growth stages (I - Initial, M - Middle, F - Final from top to bottom) of all the above pellet designs with time and progressive feeding activity under different feeding modes (columns 1-7 are extended from upper to lower half of the figure to maintain analogy). Also note for each schematic structure (not to scale) presented, there is a physical (natural) analogue recorded from the field. Also visualize the growth of structural complexities, acquisition of described barrier elements and SI index along each column from top to bottom in both the natural and schematic presentations.
FIGURE 8. A in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 8. A: Ex situ preservation (aided by wind action) of Dotilla pellets as pellet-filled burrow tubes in the supratidal flat during low tide situation. B: Ex situ preservation of Dotilla pellets (aided by wind action) in ripple troughs during low tide situation. C: Schematic profile section of the studied beach showing positions of the Dotilla pellet spread and burrow zone, spread of Ocypode burrows, mutual dispositions of different geomorphic units (dune, supratidal, upper - middle intertidal flats) relative to land - sea positions and High and Low Tide Levels (HTL and LTL). Note gradual spreading of the Dotilla pellet and burrow zone towards sea with gradual lowering of substrate water levels (WLs) during tidal recession of sea. D-E: Possible stratigraphic development of the coastal sedimentary units (1-3) and contained burrow zones and other associated features in transgressive (E) and regressive (D) situations. Note the possible position of preserved Dotilla pellets and burrows between Unit 1 and 2 under transgressiveregressive sea conditions. Features are schematic and not to scale.
FIGURE 4 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 4. Concentric - radial pellet design (Figures 3 A, C, E, G, I, K, M, O, Q, S, U and W) produced by the crab Dotilla in the upper intertidal flat of the Bakkhali beach, Bay of Bengal coast, Eastern India. Figures 3 B, D, F, H, J, L, N, P, R, T, V and Xl represent the corresponding line tracings made for measurement of Attack Index (AI) and Safety Index (SI). Figures Q and W represent conjugate concentric - radial structures made by several individuals and possesses shared concentric rows of pellets (Scrp) and very high Combined Safety index (CSI) of 97.23% and 98.33% respectively. Note the majority of the structures are made by young and adults and rarely by juveniles (example Figure 4 E, G). Also note that pellet design at the earlier stage of development has lower safety index (SI) than those in the advanced or final stage of development (SI 70.57% for Figure C vs. 100% for Figure 4 O and S). Note that structures with closed burrow opening have SI value 100% (Figure 4 G and M). Compare size of the feeding territories between A, M, O (larger for the adults) vs E (smaller for the juvenile). Scale bar equals 1 cm.
FIGURE 6 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 6. Other pellet structures produced by the crab Dotilla on the upper intertidal flat of the Bakkhali beach, Bay of Bengal coast of Eastern India. (A) Petaloid pellet design produced by petal shaped radial rows of pellets and conjugate petals formed around burrow opening. (B) The line tracing corresponding to A shows SI value 100% as the burrow mouth is closed. (C) Leaf-shaped pellet design and (D) its corresponding line tracing shows very poor SI value (13.89%). (E) Asteroid pellet design contains several radiating runways that are well enclosed within the pellet spread areas and (F) its corresponding line tracing shows 100% SI value. (G, J and K) Different stages of formation of pellet mat design in pellet – microzone 1 wherein entire surface is covered by dense population of pellets leaving no space for the predators to sneak into burrow opening (SI = 100%). Note high population density and small size of the pellet designs. (H) Mossy pellet design formed by the crab community. Several burrow openings and corresponding runways are partially to fully covered by pellet spread zones. (I) Line tracing shows variable SI values of the individual structures (marked here by red, yellow and green circles having SI values <70%, 70% - 90% and> 90% respectively) averaged at 85% for the community structure. Arrows indicate possible entry routes of predators into the burrows. (L and M) Concentric radial and concentric pellet structures formed on rippled surface. (N) At times, pellets are formed selectively along the ripple troughs. (O) Beach profile showing extends of lower, middle and upper intertidal flats, besides mudground, supratidal flat and coastal dunes. Note smaller size of the structures (G, H, J, K) due to increased population density and predation pressure.
FIGURE 2 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 2. Radial pellet structures (A, C, E, G, I and K) produced by the crab Dotilla in the upper intertidal beach of Bakkhali, Eastern India. Corresponding line tracings (B, D, F, H, J and L) are made to calculate Safety Index (SI) and Attack Index (AI). Structures represented by figures I and K suggest early stage of development of radial pellet design and possess lower Safety Index (SI = 61.53% and 46.41%, respectively) compared to other structures (A, C, E and G) that represent later stage of development of radial design and possess very high Safety Index (SI ranging from 100% to 94.74%). Figure GLeft represents a juvenile structure and the rest are produced by young and adult Dotilla. Note larger size of feeding areas made by adults (A, E, K) compared to that of juvenile (GLeft). A represents a more advanced feeding stage (over larger area) than I (over smaller area). Scale bar equals 1 cm.
FIGURE 1 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 1. Different ichnozones and geomorphic features developed in the Bakkhali (21° 33' 50" N and 88° 15' 49" E) beach of the Bay of Bengal coast, Eastern India (re-mapped in 2015 by the author and modified after De, 2019, 2000). Note field photographs of the pellet making bubbler crabs Dotilla spp. and their burrow casts.
FIGURE 5 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 5. Plan outlays for the radial, concentric and concentric-radial pellet designs produced by the bubbler crab Dotilla have been drawn from the corresponding line tracings (as referenced in each case) to highlight how structural elements are constructed to enhance burrow protection. For radial designs dense radial rows of pellets (Rrp), curved rings of pellets (Crp), pellet walls (Pw) and turned around pellet rows (Rta) are increasingly added to the structure to increase the Safety Index (SI) by closing or cutting off the probable routes of entry of the predators into the burrow openings. Note plan outlays A to D depicting gradual increase in SI values from 46.41% to 100%. Plan outlays (F. H, J, L, N and P) corresponding to the concentric pellet designs show that addition of concentrically oriented curved rings of pellets (Crp) and formation of clockwise and anticlockwise closures of the pellet rings (Cpr marked by red lines) are two basic measures taken by the crabs to enhance SI (compare the plan outlays from E to J where SI values have improved from 68.39% to 98.06%). Note that for concentric-radial designs, as displayed by the plan outlays (R, T, V and X), all the above measures, besides formation of outgoing radial pellet rows from curved rings of pellets (Crp) that act as innumerable barriers for the predators to sneak through spaces between curved rings of pellets, are taken to improve SI values (compare 70.57% for Q to 100% for W).
FIGURE 3 in Ethological interpretation of making the pellet designs by the bubbler crab Dotilla on the modern intertidal beaches: A study from the Bay of Bengal coast, Eastern India
FIGURE 3. Concentric pellet design (A, C, E, G, I, K, M, O, Q, S, U and W) produced by the crab Dotilla in the upper intertidal flat of the Bakkhali beach, Bay of Bengal, Eastern India. Figures B, D, F, H. J. L, N P, R, T, V and X represent the corresponding line tracings drawn for measurement of Attack Index (AI) and Safety Index (SI). Note formation of both clockwise and anticlockwise closures of pellet rings (Cpr), pellet walls (Pw), surface foraged (Sf), open and closed burrow openings (Obo and Cbo respectively) and curved rings of pellets (Crp). Figures L, R Left and Middle, VTop and Q correspond to concentric designs at early to middle stages of formation and possess relatively lower SI values (67.39%, 88.34%, 87.23%, 72.51% and 84.73% respectively) than the other nearly fully developed structures (SI varying between 98.06% for d to 91.04% for j). Compare size of the feeding territory between Figure I (larger for the adult) and U (smaller for the juvenile). Note SI attains 100% value for pellet designs having closed burrow opening (Cbo). Arrow heads in line tracing Figures point to possible entry routes of predators or enemies of Dotilla. Scale bar equals 1 cm.
Fig. 2 in Small mammals from barn owl Tyto alba pellets in a Mediterranean agroforestry landscape of central Italy
Fig. 2 - Dendrogram of similarity based on species frequency (algorithm: Paired group - UPGMA, Euclidean similarity index). / Dendro- gramma di similarità basato sulla frequenza di specie (algoritmo: gruppi appaiati - UPGMA, indice di similarità euclidea). Sites: / Siti: A) Roccaccia. B) Riminino. C) Ripagretta. D) San Giorgio. E) Montericcio.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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