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1,620 results for “springs”
Geometric latches enable tuning of ultrafast, spring-propelled movements
<p>The smallest, fastest, repeated-use movements are propelled by power-dense elastic mechanisms, yet the key to their energetic control may be found in the latch-like mechanisms that mediate transformation from elastic potential energy to kinetic energy. Here we test how geometric latches enable consistent or variable outputs in ultrafast, spring-propelled systems. We constructed a reduced-order mathematical model of a spring-propelled system that uses a torque reversal (over-center) geometric latch. We parameterized the model to match the scales and mechanisms of ultrafast systems, specifically snapping shrimp. We simulated geometric and energetic configurations that enabled or reduced variation of their strike durations and dactyl rotations given variation of stored elastic energy and latch mediation. We then collected an experimental dataset of the energy storage mechanism and ultrafast snaps of live snapping shrimp (<em>Alpheus</em> <em>heterochaelis</em>) and compared our simulations to their configuration. We discovered that snapping shrimp store elastic energy through deformation of the propodus exoskeleton. Regardless of the amount of variation in spring loading duration, strike durations were far less variable than spring loading durations. When we simulated this species' morphological configuration in our mathematical model, we found that the low variability of strike duration is consistent with their torque reversal geometry. Even so, our simulations indicate that torque reversal systems can achieve either variable or invariant outputs through small adjustments to geometry. Our combined experiments and mathematical simulations reveal the capacity of geometric latches to enable, reduce, or enhance variation of ultrafast movements in biological and synthetic systems. </p>
Shifts in flowering phenology in response to spring temperatures in eastern Tennessee
<p>Plant phenological shifts are among the clearest indicators of the effects of climate change. In North America, numerous studies in New England have demonstrated earlier spring flowering compared to historical records. However, few studies have examined phenological shifts in the southeastern United States, a highly biodiverse region of North America characterized by dramatic variation in abiotic conditions over small geographic areas. Here, we use 1000+ digitized herbarium records along with location-specific temperature data to examine phenological shifts of 14 spring-flowering species in two adjacent ecoregions in eastern Tennessee. We show that spring-flowering plant communities in the Blue Ridge and Ridge & Valley ecoregions differ in their sensitivity to temperature; plants in the Ridge & Valley flower 2.7 days earlier/ºC on average compared to 1.3 days/ºC for plants in the Blue Ridge. Additionally, we show that for the majority of species in both ecoregions, flowering is sensitive to spring temperature; i.e., in warmer years, most species flowered earlier. Despite this sensitivity, we did not find support for community-level shifts in flowering within eastern Tennessee in recent decades, likely because increases in annual temperature in the southeast are driven primarily by warming summer (rather than spring) temperatures. These results highlight the importance of including ecoregion as a predictor in phenological models for capturing variation in sensitivity among populations and suggest that even small shifts in temperature can have dramatic effects on phenology in response to climate in the southeastern United States.</p>
Dissolved trace metal depth profile concentrations from the North Pacific Ocean Gradients 3 (KM1906) spring 2019 cruise
<p>This dataset contains dissolved trace metal depth profiles collected in the North Pacific Ocean in spring of 2019 on the KM1906 Gradients 3 cruise (Chief Scientist E. Virginia Armbrust). The project was funded by the Simons Collaboration on Ocean Processes and Ecology Gradients program (SCOPE award 426570SP to SGJ). The samples were collected by trace metal clean rosette. Data were produced at the University of Southern California Marine Trace Elements Lab run by Prof. Seth G. John. Briefly, the metals were preconcentrated using an Elemental Scientific Inc. SeaFast automated robot, detected by Element 2 ICP-MS and quantified using isotope dilution as described in Hawco et al. 2020. <em>GCA</em> <a href="https://doi.org/10.1016/j.gca.2020.05.005">https://doi.org/10.1016/j.gca.2020.05.005</a>. All concentrations are dissolved (<0.2um SUPOR filter) and reported as nmol/L. </p>
Spring temperature drives phenotypic selection on plasticity of flowering time
<p>Data on field observations of flowering time and fitness of the perennial forest herb <em>Lathyrus vernus</em> and on spring temperature from weather station data. The dataset includes 22 years of data (1987–1996 and 2006–2017) from a <em>L. vernus</em> population located in a deciduous forest in Tullgarn, SE Sweden (58.9496 N, 17.6097 E). It includes records from 837 individuals (607 from 1987 to 1996, and 230 from 2006 to 2017), and from 2478 flowering events.</p> <p>The dataset includes the following variables:</p> <p>year: year of the recording</p> <p>id_nr: numeric plant id</p> <p>id: unique plant id (combination of numeric plant id and period)</p> <p>fcode: flowering code (1 if the plant flowered on that year, 0 if not)</p> <p>FFD: First Flowering Date</p> <p>n_fl: number of flowers</p> <p>n_fr: number of fruits</p> <p>totseed: total number of seeds</p> <p>intactseed: total number of intact seeds (not damaged by seed predator beetles)</p> <p>shoot_vol: shoot volume</p> <p>period: old (1987–1996) or new (2006–2017)</p> <p>n_years_fl_fitness: number of years when data on flowering and fitness is available</p> <p>n_years_study: number of years when the plant was included in the study</p> <p>mean_4: Average daily mean temperature of April (calculated from nearby meteorological station data)</p> <p>cmean_4: Mean-centred average daily mean temperature of April</p>
Figure 6 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 6. Pupal exuviae of Chaetocladius spp. Frontal apotome of: A) C. cf. callauensis sp. n.; B) C. mantetensis; C) C. cf. guardiolei sp. n. Distribution pattern of dorsocentral setae on thorax of: D) C. melaleucus; E) C. perennis; F) C. guisseti; G) C. mantetensis; H) C. bitusiki; I) C. cf. laminatus; J) C. cf. callauensis sp. n.; K) C. cf. guardiolei sp. n; L) C. cf. parerai sp. n.
Figure 7 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 7. Pupal exuviae of Chaetocladius spp. Caudal area of sternite VI of: A) C. melaleucus; C) C. sp. cf. laminatus. Tergite VIII (caudal part, dorsal) and anal lobe of: B) C. melaleucus; D-E) C. sp. cf. laminatus; F) C. dissipatus; G) C. sp. cf. guardiolei sp. n; L) C. cf. callauensis sp. n.; M) C. sp. cf. parerai sp. n; N) C. mantetensis. Thoracic horn of: H) C. sp. cf. laminatus; I) C. mantetensis; J) C. cf. parerai sp. n; K) C. cf. callauensis sp. n.
Figure 5 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 5. Male adult of Chaetocladius parerai sp. n. A) anal point and tergite IX in lateral view; B) scutellum; C) hypopygium, dorsal; D) hypopygium, ventral; E) gonostylus, dorsal; F) inferior volsella.
Figure 4 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 4. Male adult of Chaetocladius guardiolei sp. n. A) palpomere 3; B) clypeus; C) lobes of antepronotum; D-E) humeral pit, two aspects; F) hypopygium, anal segment and apodemes; G) anal point and tergite IX in lateral view; H) inferior volsella; I) virga, another aspect; J) gonostylus, dorsolateral.
Figure 3 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 3. Male adult of Chaetocladius callauensis sp. n. A) hypopygium, dorsal; B) hypopygium, ventral; C) anal point, dorsal; D) gonostylus, acute angle; E) gonostylus, obtuse angle; F) gonocoxite and inferior volsella, lateral; G) anal point and tergite IX in lateral view.
Figure 2 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 2. Male adult of Chaetocladius spp. C. berythensis sp. n.: A) anal point, dorsal; B) gonostylus, ventral; C) gonostylus, lateral. C. callauensis sp. n.: D) palpomere 3; E) clypeus; F) lobes of antepronotum; G) humeral pit.
Figure 1 in CHAETOCLADIUS BERYTHENSIS SP. N., C. CALLAUENSIS SP. N., C. GUARDIOLEI SP. N. AND C. PARERAI SP. N., FOUR RELICT SPECIES INHABITING GLACIAL SPRINGS AND STREAMS IN EASTERN PYRENEES AND LEBANON (DIPTERA: CHIRONOMIDAE) Abstract
Figure 1. Male adult of Chaetocladius berythensis sp. n. A) head, frontal area (right side) with temporal setae; B) palpomere 3 with sensilla clavata and sensilla coeloconica; C) details of sensilla coeloconica; D) clypeus; E) lobes of antepronotum; F) humeral pit; G-H) two aspects of anal point and tergite IX in lateral view; I) hypopygium, dorsal; J) hypopygium, ventral; K) right inferior volsella; L) gonocoxite and inferior volsella, lateral; M) gonostylus, dorsal; N) gonostylus, lateral.
Potential Predictability of the Spring Bloom in the Southern Ocean Sea Ice Zone: data and analysis scripts
<p>This repository contains the datasets and notebooks necessary to reproduce the figures in Buchovecky et al. "Potential Predictability of the Spring Bloom in the Southern Ocean Sea Ice Zone". All notebooks, except those deriving quantities from the raw model data, should work "out-of-the-box" after the appropriate local path has been set to the data.</p>
Vegetation sampling around Balma del Gai site (Moià, Barcelona, Spain); Spring 2023
<p>Vegetation sampling raw data conducted during May 2023, aimed to assess the diversity and abundance of vegetation in the vicinity of the Balma del Gai site, located in the Moià plateau, Spain. Various transects were conducted within the study area to systematically record and analyze the presence and frequency of different plant taxa.</p>
Spring temperature drives phenotypic selection on plasticity of flowering time
<p class="western">In seasonal environments, a high responsiveness of development to increasing temperatures in spring can infer benefits in terms of a longer growing season, but also costs in terms of an increased risk of facing unfavourable weather conditions. Still, we know little about how climatic conditions influence the optimal plastic response. Using 22 years of field observations for the perennial forest herb <em>Lathyrus vernus</em>, we assessed phenotypic selection on among-individual variation in reaction norms of flowering time to spring temperature, and examined if among-year variation in selection on plasticity was associated with spring temperature conditions. We found significant among-individual variation in mean flowering time and flowering time plasticity, and that plants that flowered earlier also had a more plastic flowering time. Selection favoured individuals with an earlier mean flowering time and a lower thermal plasticity of flowering time. Less plastic individuals were more strongly favoured in colder springs, indicating that spring temperature influenced optimal flowering time plasticity. Our results show how selection on plasticity can be linked to climatic conditions, and illustrate how we can understand and predict evolutionary responses of organisms to changing environmental conditions.<span><span> </span></span></p>
Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"
<p>Submitted forecasts for the 2021 Ecological Forecasting Initiative NEON Phenology Forecast Challenge and the analysis code for the accompanying manuscript. </p>
NASA's Airborne Topographic Mapper (ATM) airborne waveform and ground calibration data for the Arctic Spring campaign 2016
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM airborne waveform data from the T2 lidar, as well as the ground test data and true ranges for the Arctic Spring campaign 2016.</p> <p>A collection of MATLAB® functions to read ground test waveform and airborne waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p> <p><strong><strong>See also:</strong> </strong></p> <ul> <li>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></li> <li>User guide for NASA's Airborne Topographic Mapper HDF5 waveform data products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></li> <li>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></li> <li>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></li> <li>All ATM data products are freely available at the National Snow and Ice Data Center (NSIDC) at <a href="https://nsidc.org/data/icebridge">https://nsidc.org/data/icebridge</a> and can also be downloaded from the NASA Earthdata portal at <a href="https://earthdata.nasa.gov/">https://earthdata.nasa.gov/</a></li> <li>The ILATMW1B airborne waveform data is available at NSIDC: <a href="https://nsidc.org/data/ILNSAW1B/versions/1">https://nsidc.org/data/ILNSAW1B/versions/1</a> (narrow swath) <a href="https://nsidc.org/data/ILATMW1B/versions/1">https://nsidc.org/data/ILATMW1B/versions/1</a> (wide swath)</li> </ul>
Data from: Earlier springs increase goose breeding propensity and nesting success at Arctic but not at temperature latitudes
<p class="MsoNormal"><span>1. Intermittent breeding is an important tactic in long-lived species that trade off survival and reproduction to maximize lifetime reproductive success. When breeding conditions are unfavourable, individuals are expected to skip reproduction to ensure their own survival. <br>2. Breeding propensity (i.e. the probability for a mature female to breed in a given year) is an essential parameter in determining reproductive output and population dynamics, but is not often studied in birds because it is difficult to obtain unbiased estimates. Breeding conditions are especially variable at high latitudes, potentially resulting in a large effect on breeding propensity of Arctic-breeding migratory birds, such as geese. <br>3. With a novel approach, we used GPS-tracking data to determine nest locations, breeding propensity and nesting success of barnacle geese, and studied how these varied with breeding latitude and timing of arrival on the breeding grounds relative to local onset of spring. <br>4. Onset of spring at the breeding grounds was a better predictor of breeding propensity and nesting success than relative timing of arrival. At Arctic latitudes (> 66°), breeding propensity decreased from 0.89 (95% CI: 0.65-0.97) in early springs to 0.22 (95% CI: 0.06-0.55) in late springs, while at temperate latitudes it varied between 0.75 (95% CI: 0.38-0.93) and 0.89 (95% CI:<span> </span>0.41-0.99) regardless of spring phenology. Nesting success followed a similar pattern, and was lower in later springs at Arctic latitudes, but not at temperate latitudes.<span> </span>In early springs, a larger proportion of geese started breeding despite arriving late relative to the onset of spring, possibly because the early spring enabled them to use local resources to fuel egg laying and incubation. <br>5. While earlier springs due to climate warming are considered to have mostly negative repercussions on reproductive success through phenological mismatches, our results suggest that these effects may partly be offset by higher breeding propensity and nesting success.</span></p>
Data from: Pollinator competition and the contingency of nectar depletion during an early spring resource pulse
<p>Concerns about competition between pollinators are predicated on the assumption of floral resource limitation. Floral resource limitation, however, is a complex phenomenon involving the interplay of resource production by plants, resource demand by pollinators, and exogenous factors — like weather conditions — that constrain both plants and pollinators. In this study, we examine nectar limitation during the mass flowering of rosaceous fruit trees in early spring. Our study is set in the same region as a previous study that found extremely severe nectar limitation in summer grasslands. We use this seasonal contrast to evaluate two alternative hypotheses concerning the seasonal dynamics of floral resource limitation: either (H1) rates of resource production and consumption are matched through seasonal time to maintain a consistent degree of resource limitation or (H2) a mismatch of high floral resource production and low pollinator activity in early spring creates a period of relaxed resource limitation that intensifies later in the year. We found generally much lower depletion in our study compared to the near 100% depletion found in the summer study, but depletion rates varied markedly through diel time and across sampling days, with afternoon depletion rates sometimes exceeding 80%. In some cases, there were also pronounced differences in depletion rate across simultaneously sampled floral species, indicating different degrees of nectar exploitation. These findings generally support the seasonal mismatch hypothesis (H2) but underscore the complex contingency of nectar depletion. The challenge of future work is to discern how the fluctuation of resource limitation across diel, inter-diel, and seasonal time scales translates into population-level fitness outcomes for pollinators.</p>
Selaginella helvetica (L.) Spring (BR0000011027096)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Selaginella helvetica (L.) Spring (BR0000011026761)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
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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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