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17 results for “Microclimate measures”
Hourly gap microclimate measurements from the Coweeta Hydrologic Laboratory in 1993 and 1994
LTER Gap Project Overview Fact: Tree mortality at small spatial scales represents background levels of forest disturbance in the southern Appalachians, and is the dominant and most frequent initiator of change in terrestrial ecosystems. Hypothesis: Large-scale and rare episodic events (i.e., hurricanes, ice, etc.) may do more to influence tree replacement and stand composition in the long-run than do small scale tree mortality events. Overall Question: What is the ecological significance of small scale mortality events with respect to biotic and abiotic responses. Approach: Experimentally create typical (<300 m2) canopy gaps (girdling and herbicides) at two elevations in Rhododendron and non-Rhododendron areas. Measurements: -automated micro-environmental measurements (air and soil temperature), photosynthetically active radiation, %WC. -hemispherical photography -dendrometer bands and repeated measurements -population dynamics and seedling physiology -in situ closed core N mineralization and nitrification -small and large mammal seed and plant herbivory using exclosures Specific Questions: 1) How are microclimate and nutrient (N) cycling affected by small scale canopy removal? 2) What are the physiological and productivity responses of advanced regeneration? 3) What is the productivity response of non-gap-maker trees (dominants, co-dominant, and saplings)? 4) What strategy for recovery is most likely (seedling recruitment, sapling ingrowth, canopy closure)? 5) How do all of the above relate to/regulate each other? 6) What is the effect of elevation on response? 7) How do responses differ in Rhododendron versus non-Rhododendron areas?
Continuous microclimate measurements from Forest Site J, Coweeta Hydrologic Laboratory, North Carolina, 2007-2016.
This research involves collecting continuous soil moisture measurements on plot J of the forest gap project. In addition, air temperature, and soil temperature at 5 and 20 cm depths are also measured.
Microclimate Measurements from the Terrestrial Gradient Plots, Coweeta Hydrologic Laboratory, North Carolina
The terrestrial gradient study at Coweeta compares vegetation, soils, and understory microclimate of five sites: 118 low elevation (782 m) pine-oak, 218 low elevation (795 m) cove hardwood, 318 low elevation (865 m) mixed oak, 427 high elevation (1001 m) mixed oak, 527 high elevation (1347 m) northern hardwood. Understory microclimate stations were installed in representative locations at the downslope margin of each 20 x 40 m gradient plot (within the 80 x 80 m plot).
Microclimate proxy measurements from a logging gradient in Malaysian Borneo (BALI project)
<b>Description: </b><p>Overview<br>We focused our empirical measurements in Sabah, Borneo. We measured a microclimate thermal proxy sensitive to radiative, convective, and conductive heat fluxes. This thermal proxy is relevant to a range of organismal processes including plant regeneration, animal behavior, and soil nutrient fluxes. Measurements were made across space and time within three 1-ha plots comprising a gradient from old growth to heavy selective logging. We then combined these data with nearby weather station air temperature data, as well as measurements of topography and canopy structure derived from detailed ground surveys and airborne LIDAR.<br>Sampling design<br>Study location<br>We identified three sites along a logging intensity gradient in the Malaysian state of Sabah in Borneo. Sites each contain a 1-ha plot in lowland mixed dipterocarp forest. The plots are chosen to contrast an unlogged old growth forest with a moderately logged forest and a heavily logged forest <br>Microclimate<br>To map the microclimate thermal proxy in each plot, we installed dataloggers on a semi-regular grid pattern varying in minimum distance from 1 to 14 meters. Each datalogger was a Thermochron iButton (DS1921G, Maxim), capable of logging up to 2048 temperature values between -30°C and 70°. Each datalogger was waterproofed by wrapping in plastic paraffin film (Parafilm, Bemis) and then in light yellow duct tape. Dataloggers were attached using plastic zip-ties onto PVC stakes at a height of 1-3 cm immediately above the forest floor. The tape color was chosen to approximate the albedo of vegetation/soil, and the size of the sensor package was chosen to have a similar boundary layer to many small organisms (e.g. tree seedlings, fallen branches, large insects). The sensor packages intentionally did not include a radiation shield, as the intent was not to measure air temperature. Temperatures recorded by the loggers therefore reflect a combination of conductive, convective, and radiative heat fluxes, and can be considered a rough proxy for those experienced by small organisms. <br>Each plot contains 25 20 × 20 m2 subplots, demarcated by 1 m-high PVC stakes embedded in the soil. Each subplot also contains at its center a mesh litter trap suspended on PVC stakes at 1-m height. Exact locations for all subplot corner and center stakes were determined using ground-based Field-Map software (IFER, Jílové u Prahy, Czech Republic). Spatial positions were recorded in three-dimensional space (local x, y, z-coordinates) using an Impulse 200 Standard laser rangefinder, MapStar Module II electronic compass (Laser Technology, Colorado, USA).<br>We installed a datalogger on these stakes at the corner of each subplot and the center of each subplot. We also chose at random three focal subplots in each plot for higher-resolution sampling. Within these subplots we established a cross-type design, with six additional dataloggers deployed at 1 to 5 m distance on additional PVC stakes located near each litter trap. A total of 239 dataloggers were installed.<br>Dataloggers were deployed during the end of the dry season in late 2015. Each recorded 28 days of data at 20-minute intervals. Start times were synchronized among dataloggers within plots. The exact date of deployment was 1 November for the heavily logged plot and 9 November for the moderately logged and old growth plot. Weather conditions during November-December 2015 were consistently dry and hot, so we do not anticipate any biases from the differing start dates. In nearly all cases dataloggers were recovered in their original location, except for a small number that were transported 1-2 meters down slopes. We treated data from these as though they were in their original position. A small number of dataloggers also failed due to being lost or punctured by animal bites. 90% of dataloggers (214/239) were successfully recovered and downloaded.<br>Air temperature data<br>To compare the microclimate thermal proxy to other temperature metrics, we obtained off-plot (open site) and on-plot (below canopy) weather station data. To represent off-plot data for both the moderately and heavily logged plots, a weather station was located in a cleared area at the SAFE base camp (4.724341°N, 117.601449°E), at a distance of 2.0 km from the heavily logged plot and 3.9 km from the moderately logged plot. Data were logged continuously (Datahog, Skye Instruments, UK). Measurements included air temperature (°C) and photosynthetically active radiation (W m-2). Data were available for all of the study period. To represent off-plot data for the old growth plot, another weather station was located in a cleared area at the Maliau Basin Studies Center (4.736263°N, 116.97662°E), at a distance of 1.4 km from the plot. Available data only included photosynthetically active radiation (W m-2). Data were available for approximately 25% of the study period. We predicted air temperature values at this plot for these dates by calibrating a LOESS regression model of air temperature based on time of day (seconds after midnight) and photosynthetically active radiation, calibrated with weather station data from an open clearing at the SAFE base camp (78 km distance, 184 m lower elevation). Because of the small elevation change we did not include a further lapse rate correction for temperature. The fitted model, which had a residual standard error of 0.9°C, was used to predict off-plot air temperature at the old growth plot.<br>To represent on-plot air temperature, we located air temperature sensors (HOBO, U23-002) within radiation shields at 1.5 m height in a subplot within each plot (corresponding to a focal subplot with a higher density of microclimate dataloggers: old growth, subplot 18; moderately logged, subplot 24; heavily logged, subplot 25). Temperature was measured hourly.<br>LiDAR data<br>Discrete airborne LiDAR data were acquired by NERC's Airborne Research Facility (ARF) in November of 2014 using a Leica ALS50-II LiDAR sensor flown on a Dornier 228-20 at 41 points m-2 density, with up to four returns recorded per pulse. Georeferencing of the point cloud was ensured by incorporating data from a Leica base station in the study area. LiDAR point clouds were classified into ground and non-ground points, and used to produce a 1 m resolution canopy height model by averaging the first returns. Gaps in the canopy height model were filled by averaging neighboring cells.<br>Topography<br>The ground-mapped coordinates of the subplot corners, subplot centers, and all stems were used to construct a digital elevation model (DEM) for the plot. Elevation was interpolated onto a 1 m grid using ordinary kriging with a minimum of 4 points and search radius of 30 meters. This grid was then aligned to the LIDAR-determined location and elevation of the plot corners. The DEM was then used to estimate slope (in degrees) and cosine of aspect (with higher values indicating more southerly exposures) for each location.<br>Forest structure<br>Forest structure was determined from field surveys and from airborne laser scanning. For the field survey, all trees ≥10 cm diameter at 1.3 m height were censused in each plot in 2016. Diameter was measured at 1.3 m with a tape measure, height with laser rangefinders, and x-y position of each stem were determined using the same system as the subplot corners. The horizontal crown projection of every tree was mapped by measuring spatial positions (x and y-coordinates) of 5 to 30 points (depending on the size of the crown) at the boundary of a crown projected to the horizontal plane and then smoothed using Field-Map software. <br>Field stem maps were then converted into raster grids of stem basal area density (smoothed with 2-meter Gaussian kernel, and then rasterized to 1 m resolution), canopy density (number of overlying canopies per unit area) (1 m resolution), and plant area index (PAI) (10 m native resolution, interpolated to 1 m resolution). Spatial variation in PAI was mapped from the LiDAR point cloud using the MacArthur-Horn method. The method assumes that the leaves are randomly distributed within the laterally homogeneous canopy layers, so the PAI is proportional to the logarithm of the fraction of LiDAR pulses, β, penetrating through the canopy: PAI = -1/κ ln(β), where κ is a correction factor that accounts for canopy features, such as clumping and the distribution of leaf angles. We assumed a constant value of κ=0.7. Only the first returns, representing the first interaction of each LiDAR pulse with the canopy, are considered. We employed a lower cutoff of 2 m to avoid confusing ground returns with low-lying vegetation. PAI was estimated for point locations along a 1 m regular grid using circular sampling neighborhood of 10 m. This sampling window size is used to capture a sufficient number of LiDAR returns to avoid saturation effects in the more densely vegetated parts of the plots. This approach for calculating canopy closure may be biased, as clumping of vegetation, variation in leaf angle, and canopy edges (i.e. at gaps) should lead to spatial variation in the κ coefficient. It was not possible with our data to constrain κ using hemispherical photos due to saturation effects.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/22"><b>Drivers of microclimate variation in disturbed forests</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC independent research fellowship (Standard grant, NE/M019160/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000/2/2(JLD.3((126) )</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3266822">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Blonder_Mircoclimate.xlsx, Mircoclimate_proxy.zip</p><p><b>Blonder_Mircoclimate.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mircoclimate datalogger data</b> (described in worksheet Data_Sheet_S1)</p><p>Description: Raw temperature measurements for all microclimate dataloggers</p><p>Number of fields: 7</p><p>Number of data rows: 444416</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Tag</b>: The field code for the data logger (Field type: ID)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Forest.floor.temperature..degC.</b>: Microclimate proxy reading (Field type: Numeric)</li><li><b>X..m.</b>: Location of the data logger in meters east in UTM zone 50N (Field type: Numeric)</li><li><b>Y..m.</b>: Location of the data logger in meters north in UTM zone 50N (Field type: Numeric)</li><li><b>Z..m.</b>: Location of the data logger in meters above sea level (Field type: Numeric)</li></ul></li><li><p><b>Weather station data</b> (described in worksheet Data_Sheet_S3)</p><p>Description: Raw off-plot and on-plot weather station air temperature data</p><p>Number of fields: 4</p><p>Number of data rows: 1397</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Off.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the off-plot weather station (Field type: Numeric)</li><li><b>On.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the on-plot weather station (Field type: Numeric)</li></ul></li></ol><p><b>Mircoclimate_proxy.zip</b></p><p>Description: S2 Spatial datasets for microclimate, topography, and canopy structure. All datasets are spatially interpolated to 1m resolution and projected into UTM coordinates, zone 50N. </p><p><b>Date range: </b>2015-11-01 to 2015-12-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Microclimate measurements (15-minute intervals) at Fish Lake Environmental Education Center (Eastern Michigan University; Lapeer County, Michigan, USA)
Open the record for dataset details and reuse information.
Weather and Terrestrial Microclimate Measurements from sites located at Mars Hill University
This project contains data collected from sites located on the campus of Mars Hill University in Mars Hill, NC. Weather data includes measurements of air temperature, relative humidity, wind speed and direction, precipitation, and photosynthetically active solar radiation. Soil moisture, soil temperature, and air temperature data were measured at two forested locations. At each forested location, eight soil moisture sensors were installed in transects radiating NE, NW, SE, and SW from the datalogger. Sensors were installed approximately 20m and 40m along the transect. Measurements were taken every minute and hourly and daily data values recorded.
Supplementary material 5 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
Explanation of header and data: Dist is the distance to the zero line (edge) into the forest in m; Month is the period of time before litterfall was sampled (see "Litterfall" for details); DryMass is the weight of the dried litter in g, Site is either west-facing or east-facing (see "Measurement site" for details).
Supplementary material 3 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
Explanation of header and data: Dist is the distance from the zero line (edge) into the arable land in m, 0 is the zero line; Crop refers to the species (see "Biomass of crops" for more details); DryMass is given in g per m2.
Supplementary material 4 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
Explanation of header and data: Site is either west-facing or east-facing site (see "Measurement site"); Dist is the distance from the zero line (edge) to the forest interior in m, 0 is the zero line; Tree number refers to a unique tree (number) in the plot (letter); Perimeter is measured at 1.30 m from the ground and is given in cm; BHD is derived from the perimeter and given in cm; Height is the measured height of the trees in m.
Supplementary material 6 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
Explanation of header and data: Site is either west-facing or east-facing (see "Measurement site"); DistToEdge is the distance to the zero line (edge) in m, negative values are in the forest, positive values are in the arable land, zero is the edge; Repetition is the number of repetitions in the lab; Depth is measured in cm and is the depth of soil sampling ±3 cm; Ctot is the percentage (%) of total soil carbon content in the tested soil sample; Ntot is the percentage (%) of total soil nitrogen content in the tested soil sample and pH is the numeric scale to specify the acidity or basicity of the soil sample in solution.
Supplementary material 2 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
Measured values are indicated in the header by the variable (e.g. "SoilMoist") followed by the distance to the zero line (e.g. 30, indicated by XX in the description below). Date.Time is given as YYYY-MM-DD HH:MM:SS; SoilMoistXX is the soil moisture given in cm3 cm-3; SoilTempXX is the soil temperature given in °C; RelHumXX is the relative humidity given as dimensionless number; AirTempXX is the air temperature in °C; AirPressXX is the barometric air pressure given in kPa; SolarRadXX is the solar radiation given in W m-2; WindAvgXX is the average wind speed given in m s-1; WindMaxXX is the maximum wind speed given in m s-1; WindDirXX is the direction of the wind given in °; PrecXX is the precipitation given in mm; DistXX is the distance to the zero line (edge), positive values are in the arable land, negative values are in the forest, zero is the edge. For more details see "Microclimate". The data was edited according to "Data converting". Timezone: Central European Time (CET).
Supplementary material 1 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
Measured values are indicated in the header by the variable (e.g. "SoilMoist") followed by the distance to the zero line (e.g. 30, indicated by XX in the description below). Date.Time is given as YYYY-MM-DD HH:MM:SS; SoilMoistXX is the soil moisture given in cm3 cm-3; SoilTempXX is the soil temperature given in °C; RelHumXX is the relative humidity given as dimensionless number; AirTempXX is the air temperature in °C; AirPressXX is the barometric air pressure given in kPa; SolarRadXX is the solar radiation given in W m-2; WindAvgXX is the average wind speed given in m s-1; WindMaxXX is the maximum wind speed given in m s-1; WindDirXX is the direction of the wind given in °; PrecXX is the precipitation given in mm; DistXX is the distance to the zero line (edge), positive values are in the arable land, negative values are in the forest, zero is the edge. For more details see "Microclimate". The data was edited according to "Data converting". Timezone: Central European Time (CET).
Microclimate measurement, HOBO stations at permanent plots, sites
HOBO micromet station are located at the three permanent plots, presidents house, desert botanical garden, and community services building. All stations are equiped with temperature, humidity, and PAR sensors. The station at the DBG is part of an experimental setup in which different watering regimes are tested. At the presidents house soil temperatures and moisture is measured at 3 depth. At the community services building soil temperature and moisture are measured at the same depth under a shrub, in a swale and a slope.
Supplementary material 7 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
R Script for converting of microclimatic data
Measuring RNA editing through mmPCR-seq in Drosophila adapting to divergent microclimates and raised at different temperatures
GEO Series GSE104084. Drosophila melanogaster. 128 samples. Type: Other.
Measuring gene expression and RNA editing in Drosophila adapting to divergent microclimates
GEO Series GSE104073. Drosophila melanogaster. 64 samples. Type: Expression profiling by high throughput sequencing.
Parameterization of grass grid pavers for urban microclimate modelling to simulate the heat mitigation potential of unsealing measures in cities
<p><strong>README: </strong></p> <p>The exact version (V 5.1.2 Compatibility Release) of the ENVI-met model used for this paper, the parameterizations and the input data to run the ENVI-met model and to reproduce the model outputs of all the performed scenario simulations shown in this paper, as well as all the scripts for post-processing of the model output data needed to derive the presented results of this manuscript are provided in this permanent archive. The supplementary material (data and code) includes the following files: </p> <p>Appendix_1: ENVI-met model code of the version used to reproduce the outputs of this paper (V 5.1.2 Compatibility Release): <br> Appendix_1.exe: ENVImet model Version 5.1.2 setup </p> <p>Appendix_2: Model input data: for GGP scenarios including the model database parameterizations: <br> Appendix_2.1.zip: Input data for extreme scenario <br> Appendix_2.2.zip: Input data for realistic scenario <br>Eeach zip file contains: model driver file for LBCs, project file, project database, model domain file, and simulation task file. </p> <p>Appendix_3: Python scripts for postprocessing and extraction of model output data: <br> Appendix_3.1.enpy: Python script for exporting surface data <br> Appendix_3.2.enpy: Python script for exporting atmosphere data and soil data</p> <p>Appendix_4: R script for data analyses of model results: <br> Appendix_4.rmd: R script for data analyses of extracted simulation results and plotting </p> <p> </p>
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