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1,620 results for “springs”
Late to bed, late to rise—Warmer autumn temperatures delay spring phenology by delaying dormancy
<p>Spring phenology of temperate forest trees has advanced substantially over the last decades due to climate warming, but this advancement is slowing down despite continuous temperature rise. The decline in spring advancement is often attributed to winter warming, which could reduce chilling and thus delay dormancy release. However, mechanistic evidence of a phenological response to warmer winter temperatures is missing. We aimed to understand the contrasting effects of warming on plants leaf phenology and to disentangle temperature effects during different seasons.</p> <p>With a series of monthly experimental warming by ca. 2.4 °C from late summer until spring, we quantified phenological responses of forest tree to warming for each month separately, using seedlings of four common European tree species. To reveal the underlying mechanism, we tracked the development of dormancy depth under ambient conditions as well as directly after each experimental warming. In addition, we quantified the temperature response of leaf senescence.</p> <p>As expected, warmer spring temperatures led to earlier leaf-out. The advancing effect of warming started already in January and increased towards the time of flushing, reaching 2.5 days/°C. Most interestingly, however, warming in October had the opposite effect and delayed spring phenology by 2.4 days/°C on average; despite six months between the warming and the flushing. The switch between the delaying and advancing effect occurred already in December. We conclude that not warmer winters but rather the shortening of winter, i.e. warming in autumn, is a major reason for the decline in spring phenology.</p>
Baliles Center (Hull Springs) Depth at Dock from 2021-10-30 to 2021-12-04
<p>General Metadata for Hull Springs Dock-YSI Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HS_YSI_YYYY-MM-DD_metadata.txt HS_dock_pressure_trans_YYYY-MM-DD_metadata.txt</code></pre> <p> </p> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning.</p> <p>File Created</p> <ul> <li>2021-07-14 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added metadata for pressure transducer</li> </ul> <p>Description</p> <p>These data are from the sampling station in Aimes Creek on the Camp House dock (38.125367, -76.659537).</p> <pre><code>* Physical and chemical water data are collected with a YSI EXO2 sonde. * Temperature (dC) and Pressure (KPa) are collected with an Onset HOBO U20-001-01-Ti Water Level Logger</code></pre> <p> </p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The sensors are sampled every 15 minutes</p> <p>Measurements Parameters, units, and Variable Names</p> <ul> <li>date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS)</li> <li>date.time.adj - the date and time that the pressure was measured adjusted to match the time that barometric pressure was collected. (YYYY-MM-DD HH:MM:SS)</li> <li>observation - the number of the observation.</li> <li>timestamp - the date and time as reported by the data logger (MM/DD/YY HH:MM:SS AM/PM)</li> <li>date - the date that the record was collected (MM/DD/YYYY)</li> <li>time - the time that the record was collected (HH:MM:SS)</li> <li>site_name - the ID name provided by the KOR software.</li> <li>unit_ID - the sonde identification number.</li> <li>user_ID - the user that created the sampling template.</li> <li>Temp_dC - the water temperature in degrees C.</li> <li>DO_perc - the dissolved oxygen percent saturation (%).</li> <li>DO_percL - the "local" dissolved oxygen percent saturation where the calibration is locally always 100% saturated independent of the barometric pressure (%).</li> <li>DO_mg-L - the concentration of dissolved oxygen (mg/L).</li> <li>SPC_uS-cm - the specific conductance (uS/cm).</li> <li>C_uS-cm - the conductivity (uS/cm).</li> <li>nLFC_uS-cm</li> <li>TDS_mg-L - the total dissolved solids (mg/L).</li> <li>SAL_PSU - the salinity of the water (PSU).</li> <li>pH - the pH of the water.</li> <li>pH_mV - the millivolt measurement of the pH meter (mV).</li> <li>FNU - the turbidity of the water (FNU).</li> <li>TSS_mg-L - the total suspended solids (mg/L).</li> <li>BGA_PC_RFU - the phycocyanin fluorescence level which is an indicator of blue-green algae (RFU).</li> <li>BGC_PC_ug-L - the concentration of phycocyanin in the water, which is an indicator of blue-green algae (ug/L).</li> <li>Chl_RFU - the chlorophyll fluorescence level, which is an indicator of phytoplankton biomass (RFU).</li> <li>Chl_ug-L - the concentration of chlorophyll in the water, which is an indicator of phytoplankton biomass (ug/L).</li> <li>fDOM_RFU - the fluorescent DOM fluorescence level (RFU).</li> <li>fDOM_QSU - the standardized fluorescent DOM concentration (QSU).</li> <li>Wiper_V - the voltage output of the wiper (V).</li> <li>Cabel_V - the voltage output of the cable (V).</li> <li>Batt_V - the voltage output of the internal batteries (V).</li> <li>Pressure - the pressure of the water above the pressure transducer (KPa)</li> <li>Temp - the water temperature reported by the pressure transducer (dC)</li> <li>press_mmHg - the pressure of the water above the pressure transducer (mm Hg)</li> <li>BP_mmHg - the barometric pressure recorded by the weather station at the Yellow House (mm Hg)</li> <li>Z_press_trans - the depth of the water above the pressure transducer (cm)</li> <li>Z - the depth of the water above the sediments (cm)</li> </ul>
Baliles Center (Hull Springs) Wetland Data 2021-10-28 to 2021-12-04
<p>General Metadata for Hull Springs Restored Wetland Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_Depth_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt</code></pre> <p> </p> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-06-16 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor.</li> <li>2021-11-10 by KF - updated to include the depth calculations from the water level logger.</li> </ul> <p>Description</p> <p>These data are from the sampling station in the restored wetland at the Baliles Center for Environmetal Education at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p> </p> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
The model data of Potential Impact of Spring Thermal Forcing over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation
<p>This is the model data of "Potential Impact of Spring Thermal Forcing over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation". The data includes the last 20 years data of control run (CTRL), the 20 years data of TP–T experiment, and the wave activity flux difference between ensemble means of TP–T and CTRL. 2D is two dimensions. 3D is three dimensions.</p>
Mildew ratings and yields of winter wheat and spring oat varieties on NIAB and AHDB Recommended Lists, 1972-2022
<p>Data on powdery mildew ratings and yields in fungicide-treated trials relative to controls, for winter wheat and spring oat varieties on UK Recommended Lists from 1972 to 2022. These data are used in the graphs in Figure 1 of Brown & Wulff (2022) 'Diversifying the menu for crop powdery mildew resistance', Cell, DOI https://doi.org/10.1016/j.cell.2022.02.003. Data are compiled from published information. (c) NIAB for data from 1972 to 2001. (c) Agriculture and Horticulture Development Board </p>
Stimulation, reduction and compensation growth, and variable phenological responses to spring and/or summer-autumn warming in Corylus taxa and Cornus sanguinea L.
<p>Two datasets containing data from three Corylus taxa (Corylus avellana, Corylus maxima and intermediate forms) and from Cornus sanguinea. Plants, in a common garden setting, were subjected to a periodic warming experiment in a greenhouse environment in 2018. </p>
dataset from paper with title: "Direct Phenological Responses but Later Growth Stimulation upon Spring and Summer/Autumn Warming of Prunus spinosa L. in a Common Garden Environment"
<p>abstract of paper: "Future<strong> </strong>predictions of forest ecosystem responses are a challenge, as global temperatures will further rise in the coming decades at an unprecedented rate. The effect of elevated temperature on growth performance and phenology of three <em>Prunus spinosa </em>L. provenances (originating from Belgium, Spain, and Sweden) in a common garden environment was investigated. One-year-old seedlings were grown in greenhouse conditions and exposed to ambient and elevated temperatures in the spring (on average 5.6 °C difference) and in the late summer/autumn of 2018 (on average 1.9 °C difference), while they were kept hydrated, in a factorial design. In the following years, all plants experienced the same growing conditions. Bud burst, leaf senescence, height, and diameter growth were recorded. Height and radial growth were not affected in the year of the treatments (2018) but were enhanced the year after (2019), whereas phenological responses depended on the temperature treatments in the year of the treatments (2018) with little carry-over effects in the succeeding years. Spring warming enhanced more height growth in the succeeding year, whereas summer/autumn warming stimulated more radial growth. Spring warming advanced bud burst and shortened the leaf opening process whereas summer/autumn warming delayed leaf senescence and enlarged the duration of this phenophase. These results can help predict the putative shifts in species composition of future forests and woody landscape elements."</p>
Baliles Center (Hull Springs) Weather Data from 2021-10-28 to 2021-12-04
<p>General Metadata for Hull Springs Farm Atmospheric Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HSF_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>NOTE: The the TIMESTAMP variable is incorrect. The correct time is in the TIMESTAMP.corr variable. See the metadata file for more information.</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Pulsed stress hypothesis revisited – A case study of Metopolophium dirhodum and spring wheat
<p>Life table data for the rose-grain aphid, Metopolophium dirhodum, reared on the spring wheat, Triticum sativum, under four regimes of water supply: 40C - continuous drought (40 SWC), 70C - well watered (70 SWC), 40-0, 40-1, 40-2 and 40-3 - pulsed stress timed for one week before aphids were established (40-0), during nymphal development (40-1), and during first (40-2) and second week of reproduction (40-3). </p>
Data from: Shifts in growth light optima among diatom species support their succession during the spring bloom in the Arctic
<p>Diatoms of the Arctic Ocean annually experience extreme changes of light environment linked to photoperiodic cycles and seasonal variations of the snow and sea-ice cover extent and thickness which attenuate light penetration in the water column. Arctic diatom communities exploit this complex seasonal dynamic through a well-documented species succession during spring, beginning in sea-ice and culminating in massive phytoplankton blooms underneath sea-ice and in the marginal ice zone. The pattern of diatom taxa sequentially dominating this succession is relatively well conserved interannually, and taxonomic shifts seem to align with habitat transitions.</p> <p>To understand whether differential photoadaptation strategies among diatom taxa explain these recurring succession sequences, we coupled lab experiments with field work in Baffin Bay at 67.5°N. Based on field data, we selected five diatom species typical of different ecological niches and measured their growth rates under light intensity ranges representative of their natural habitats. To characterize their photoacclimative responses, we sampled pigments and total particulate carbon, and conducted <sup>14</sup>C-uptake photosynthesis response curves and variable fluorescence measurements.</p> <p>We documented a gradient in species respective light intensity for maximal growth suggesting divergent light response plasticity, which for the most part align with species sequential dominance. Other photophysiological parameters supported this ecophysiological framing, although contrasts were always clear only between succession endmembers, <em>Nitzschia frigida</em> and <em>Chaetoceros neogracilis</em>. To validate that these photoacclimative responses are representative of in situ dynamics, we compared them to the chlorophyll <em>a</em>-specific light-limited slope (<em>α</em>*) and saturated rate of photosynthesis (<em>P</em>*<sub><em>M</em></sub>), monitored in Baffin Bay on sea-ice and planktonic communities. This complementary approach confirmed that unusual responses in <em>α</em>* and <em>P</em>*<sub><em>M</em></sub> as a function of light history intensity are similar between sentinel sympagic species <em>N. frigida</em> and natural ice-core communities. While no light-history-dependent trends were observed in planktonic communities, their <em>α</em>* and <em>P</em>*<sub><em>M</em></sub> values were in the range of measurements from our monospecific cultures. </p> <p><strong><em>Synthesis.</em></strong> Our results suggest that Arctic diatoms species photoadaptation strategy is tuned to the light environment of the habitats in which they dominate and indeed drives the seasonal taxonomic succession.</p>
Microclimate-driven trends in spring-emergence phenology in a temperate reptile (Vipera berus): Evidence for a potential 'climate trap'?
<p>Climate change will increase the exposure of organisms to higher temperatures, but can also drive phenological shifts that alter their susceptibility to conditions at the onset of breeding cycles. Organisms rely on climatic cues to time annual life-cycle events, but the extent to which climate change has altered cue reliability remains unclear. Here, we examine the risk of a 'climate trap' – a climatically-driven desynchronisation of the cues that determine life-cycle events and fitness later in the season in a temperate reptile, the European adder (<em>Vipera berus)</em>. During the winter, adders hibernate underground, buffered against sub-zero temperatures, and re-emerge in the spring to reproduce. We derived annual spring-emergence trends between 1983 and 2017 from historical observations in Cornwall, United Kingdom, and related these trends to the microclimatic conditions that adders experienced. Using a mechanistic microclimate model, estimates of below- and near-ground temperatures were used to derive accumulated degree-hour and absolute temperature thresholds that predicted annual spring-emergence timing. Trends in annual emergence timing and subsequent exposure to ground frost were then quantified. We found that adders have advanced their phenology towards earlier emergence. Earlier emergence was associated with increased exposure to ground frost and, contradicting the expected effects of macroclimate warming,<em> </em>increased post-emergence exposure to ground frost at some locations. The susceptibility of adders to this 'climate trap' was related to the rate at which frost risk diminishes relative to advancement in phenology, which depends on the seasonality of climate. We emphasise the need to consider exposure to changing microclimatic conditions when forecasting biological impacts of climate change.</p>
SCALE Spring Radiation fluxes over sea ice at MIZ2
<p>Radiation fluxes measured by a Kipp & Zonen CNR4 Net Radiometer. The radiometer was deployed on a mast on a sea ice floe at 59.5 S, 0E on 24-25 October 2019. For further details, please refer to the SCALE Cruise Report.</p> <p>Note that data collected before 12:30 GMT on 24th October should be treated with caution as there may be some effects from the ship, which was in the vicinity up until this time. </p>
Supplementary data for: Effects of thermal acclimation on the proteome of the planarian Crenobia alpina from an alpine freshwater spring
<p>Species' acclimation capacities and their ability to maintain molecular homeostasis outside of ideal temperature ranges will partly predict their success following climate-change induced thermal regime shifts. Theory predicts that ectothermic organisms from thermally stable environments have muted plasticities, and that these species <span>may be</span> particularly vulnerable to temperature increase. Whether such species retained or lost acclimation capacities remains largely unknown. We studied proteome changes in the planarian <em>Crenobia alpina</em>, a prominent member of cold-stable alpine habitats that is considered to be cold-adapted stenotherm. We found that the species' CT<sub>max</sub> is above its experienced habitat temperatures and that different populations exhibit differential CTmax acclimation capacities, whereby an alpine population showed reduced plasticity. In a separate experiment, we acclimated <em>C. alpina</em> individuals from the alpine population to 8, 11, 14, or 17°C over the course of 168 h and compared a comprehensively annotated species-specific proteome. Network analyses of 3399 proteins and protein set enrichment show that while the species' proteome is overall stable across these temperatures, protein sets functioning in oxidative stress response, mitochondria, protein synthesis and turnover are lower abundant following warm acclimation. Proteins associated with an unfolded protein response, ciliogenesis, tissue damage repair, development, and the innate immune system were higher abundant following warm acclimation. Our findings suggest that this species has not suffered DNA decay (e.g., loss of heat-shock proteins) during evolution in a cold-stable environment and retained plasticity in response to elevated temperatures, challenging the notion that stable environments necessarily result in muted plasticity.</p>
Colman et al. Subsurface Archaea Associated with Rapid Geobiological Change in a Model Yellowstone Hot Spring - Supplementary Datasets
<p>These datasets comprise the supplementary datasets for Colman et al. (2022) Subsurface Archaea Associated with Rapid Geobiological Change in a Model Yellowstone Hot Spring, <em>Communications Earth & Environment </em></p>
Data and code from: Great tits differ in glucocorticoid plasticity in response to spring temperature
<p>No description provided.</p> <p>This repository contains R-codes used for a project on glucocorticoid plasticity using data from a free-living great tit population. Specifically, the repository contains R-codes used in the following study:</p> <p>Hau M, Deimel C, Moiron M (2022) Great tits differ in glucocorticoid plasticity in response to spring temperature.BioRxiv (doi: 10.1101/2022.04.21.489013) Link to paper: <a href="https://www.biorxiv.org/content/10.1101/2022.04.21.489013v1">https://www.biorxiv.org/content/10.1101/2022.04.21.489013v1</a></p> <p>For any further information, please get in touch with at least one (preferably both) of the following researchers: Dr. Maria Moiron, email: <a href="mailto:mariamoironc@gmail.com">mariamoironc@gmail.com</a> Prof. Dr. Michaela Hau, email: <a href="mailto:mhau@orn.mpg.de">mhau@orn.mpg.de</a></p> <p>Code: All R code is available in the main folder and scripts are numbered in order of use from 001 to 004. Scripts titles should be self-explanatory, but each script contains a "Description of script and Instructions" section with further information.</p>
Noah-MP data for modeling Canadian spring wheat study
<p>This zip file contains the simulation results from a Noah-MP crop model for a Canadian spring wheat study.</p> <p>There are two separate folders inside: one for single-point data and one for regional data results.</p> <p>The single-point folder contains three model outputs from the three site-year (2016, 2019SW, 2019SE) and three model treatments (default NoahMP, wheat model, and TAVE for dynamic planting threshold)</p> <p>The regional folder contains the combined agricultural statistics from USDA and StatisCanada (combine_crop_PPR.nc), default wheat model results, and the temperature stress results. </p> <p>Please feel free to contact Dr. Zhe Zhang (zhe.zhang@usask.ca) or Dr. Yanping Li (yanping.li@usask.ca) for further details.</p>
Spring phenology and pathogen infection affect multigenerational plant attackers throughout the growing season
<p>Climate change has been shown to advance spring phenology, increase the number of insect generations per year (multivoltinism), and increase pathogen infection levels. However, we lack insights into the effects of plant spring phenology and the biotic environment on the preference and performance of multivoltine herbivores and whether such effects extend into the later part of the growing season. To this aim, we used a multifactorial growth chamber experiment to examine the influence of spring phenology on plant pathogen infection, and how the independent and interactive effects of spring phenology and plant pathogen infection affect the preference and performance of multigenerational attackers (the leaf miner Tischeria ekebladella and the aphid Tuberculatus annulatus) on the pedunculate oak in the early, mid and late parts of the plant growing season. Pathogen infection was highest on late phenology plants, irrespective of whether inoculations were conducted in the early, mid or late season. The leaf miner consistently preferred to oviposit on middle and late phenology plants, as well as healthy plants, during all parts of the growing season, whereas we detected an interactive effect between spring phenology and pathogen infection on the performance of the leaf miner. Aphids preferred healthy, late phenology plants during the early season, healthy plants during the mid season, and middle phenology plants during the late season, whereas aphid performance was consistently higher on healthy plants during all parts of the growing season. Our findings highlight that the impact of spring phenology on pathogen infection and the preference and performance of insect herbivores is not restricted to the early season, but that its imprint is still present – and sometimes equally strong – during the peak and end of the growing season. Plant pathogens generally negatively affected herbivore preference and performance, and modulated the effects of spring phenology. We conclude that spring phenology and pathogen infection are two important factors shaping the preference and performance of multigenerational plant attackers, which is particularly relevant given the current advance in spring phenology, pathogen outbreaks and increase in voltinism with climate change. </p>
Magazine Spring, Historic Halifax (before)
View of the Magazine Spring at Historic Halifax State Historic Site in July 2018 prior to recent cleaning (6/8/2021). This model was constructed by David Cranford with 19 photos using Agisoft Metashape software. (Credit: NCDNCR/OSA) Source: Objaverse 1.0 / Sketchfab
Bradfordville CW Marker Spring 2017
The American Civil War monument dedicated to General Lee located near Bradfordville, FL. The photos that created this model were taken on the spring of 2017. I did a new model because the monument had been knocked over (apparently due to carelessness rather than vandalism) and re-set. I wanted to document changes and damage and use this as an opportunity to test the CloudCompare software. As a result, I used the same camera and creation process to keep things as similar as possible. * [2016 version](https://sketchfab.com/models/8e753b01f9594236bc2840f53c5d10e1) * [Base-aligned comparison](https://sketchfab.com/models/89bc45eca9ae4b7b8ff5513dd9778fc8) * [Face-aligned comparison](https://sketchfab.com/models/384dfb5f86a34193ab5e542f72bd4a8c) Details * Created using Agixoft PhotoScan. * Made from 72 Photos taken with a Canon PowerShot A2500. * Photos and modeling by Tristan Harrenstein Source: Objaverse 1.0 / Sketchfab
SSU rRNA Gene Analysis of the Environmental and Biological Influences on Carbonate Precipitation within Hot Spring Microbial Mats in Little Hot Creek, CA
<p>R Markdown notebook and all data required to recreate the 16S rRNA Gene analysis figures found in Wilmeth et. al (In Submission), including mapping files, BIOM files, and R markdown notebook. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.