Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Figure 6 in A small slug from a tropical greenhouse reveals a new rathouisiid lineage with triaulic tritrematic genitalia (Gastropoda: Systellommatophora)

Figure 6. Maximum likelihood (ML) tree of concatenated COI + 16S rDNA haplotypes obtained from Barkeriella museensis gen. et sp. nov. and Rathouisia sinensis compared with sequences deposited in GenBank for representatives of the families Onchidiidae, Veronicellidae and Philomycidae (see Supporting Information, Tables S1, S4 for sequences obtained from GenBank for analysis). Concatenated sequences are listed with species names. They were 1028 positions in length (573 COI + 455 16S rDNA). Bootstrap support above 50% from ML (left) and NJ (middle) analysis, as well as posterior probabilities PP above 0.5 from Bayesian Inference analysis (right) are indicated next to the branches. Bootstrap analysis was run with 1000 replicates (Felsenstein, 1985). The tree was rooted with stylommatophoran Monacha pantanellii sequences deposited in GenBank by us and by Pieńkowska et al. (2020) according to Table 3 and Supporting Information, Table S1, respectively.

opencc-by-4.0Feb 2023View details →

Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.

Dataset used in <a href="https://link.springer.com/article/10.1007/s10533-023-01104-0?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231214&utm_content=10.1007/s10533-023-01104-0#citeas">Elevated temperature and nutrients lead to increased N2O emissions from salt marsh soils from cold and warm climates</a>. The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocatière: 47°22'24.7"N 70°03'26.3"W) and Louisiana, U.S. (Barataria Basin: 29°33'47.3"N 90°04'22.8"W and 29°29'52.2"N 89°55'00.2"W) from two vegetation types (Sporobolus alterniflorus formerly known as Spartina alterniflora and Sporobolus pumilus formerly known as Spartina patens). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21st July 2021 from Louisiana and the 9-10th August 2021 from Quebec. Environmental data was collected including in-situ soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d13C and soil d15N determined from additional 0-15 cm core samples. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1). Stable 15N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving 15N-NO3‑ for denitrification rates and one core receiving 15N-NH4+ for nitrification rates. The cores were incubated at ambient temperature (16 ℃ and 28.1 ℃ for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO3-, 2.0 NH4+; 2.9 NO3-, 2.5 NH4+; 0.5 NO3-, 7.3 NH4+ and 5.7 NO3-, 2.8 NH4+ mg g wet soil-1 for Quebec S. alterniflorus, Quebec S. pumilus, Louisiana S. alterniflorus and Louisiana S. pumilus, respectively), and elevated temperature (ambient temperature +5 ℃) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N2O, CH4, CO2) and 15N in denitrification-derived N2O, denitrification-derived N2 and nitrification-derived N2O. Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer. Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 MΩ) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO3- and NH4+, one subsample was extracted with Olsen-P solution (0.5 M NaHCO3, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d13C and d15N. N2O, CH4 and CO2 concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler (Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH4 analysis and a micro electron capture detector (mECD) for N2O analysis. CO2 was methanised to CH4 before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N2O, 72 ppb CH4 and 31 ppm CO2. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see (https://doi.org/10.1002/2017JG003783). The 15N content of the N2 and N2O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d15N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l-1 and 10 mg l-1 standards resulting in accuracy and precision of 0.3 and ±0.3 mg C l-1, and 0.5 and ±0.3 mg N l-1, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (<a href="https://doi.org/10.1080/00103629509369298">https://doi.org/10.1080/00103629509369298</a>) with a limit of detection of 0.1 ppm and accuracy of ±5 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO3, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l-1 and accuracy of ±6 %. Soil total carbon, total nitrogen, d13C and d15N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d13C and d15N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.

opencc-by-4.0Apr 2023View details →

Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions

Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>. Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.

opencc-by-4.0May 2023View details →

Bioverse: The Habitable Zone Inner Edge Discontinuity as an Imprint of Runaway Greenhouse Climates on Exoplanet Demographics

This repository contains data required to run the <a href="https://github.com/matiscke/hz-inner-edge-discontinuity">pipeline producing the results and figures in Schlecker+2023</a>, in particular results objects created with expensive model grid runs of <a href="https://github.com/danielapai/bioverse">Bioverse</a>.

opencc-by-4.0Sep 2022View details →

Data used in the figures of the article "A cool runaway greenhouse without surface magma ocean"

These data have been generated with the codes Exo k and PCM. Exo k is an open-source software. A complete documentation on how to install and use it can be found at http://perso.astrophy.u-bordeaux.fr/&sim;jleconte/ exo k-doc/index.html. The generic Global Climate Model PCM (formerly known as LMDZ.generic) used in this work is the version 2528 that can be downloaded with documentation from the SVN repository at https://svn.lmd.jussieu.fr/Planeto/trunk/ LMDZ.GENERIC/. More information and documentation are available at http://www-planets.lmd.jussieu.fr. The archive is organized as follows: ├─ Fig1 (comparison of atmospheric profiles with a same Outgoing Thermal Radiation) │  ├─ OTR274.txt (profiles with a 274 W/m2 bolometric emission) │  ├─ OTR10000txt (profiles with a 1000 W/m2 bolometric emission) │ ├─ Fig2 (Outgoing Thermal Radiation as a function of surface temperature) │  ├─ Geoflux=0 (internal heat flux = 0 W/m2) │  │  ├─ T1_0_ISR_Tsurf_OTR_ALB.dat │  │  ├─ Proxima_0_ISR_Tsurf_OTR_ALB.dat │  │  ├─ M3_0_ISR_Tsurf_OTR_ALB.dat │  │  ├─ Sun_0_ISR_Tsurf_OTR_ALB.dat │  │  ├─ F1_0_ISR_Tsurf_OTR_ALB.dat │  ├─ Geoflux=0.09Wm-2 (internal heat flux = 0.09 W/m2) │     ├─ T1_0.09_ISR_Tsurf_OTR_ALB.dat │     ├─ Sun_0.09_ISR_Tsurf_OTR_ALB.dat │ ├─ Fig3 (Trappist-1b profiles and spectra) │  ├─ Profiles │  │  ├─ Adiabatic │  │  │   ├─ Trappist1b_adiabatic_profile_1EO.txt (1x Earth Ocean) │  │  │   ├─ Trappist1b_adiabatic_profile_3EO.txt (3x Earth Ocean) │  │  │ │  │  ├─ Converged │  │  │   ├─ Trappist1b_profile_1EO_geoflux=0W.txt (1x Earth Ocean, internal heat flux = 0 W/m2) │  │  │   ├─ Trappist1b_profile_1EO_geoflux=5W.txt (1x Earth Ocean, internal heat flux = 5 W/m2) │  │  │   ├─ Trappist1b_profile_3EO_geoflux=0W.txt (3x Earth Ocean, internal heat flux = 0 W/m2) │  │  │   ├─ Trappist1b_profile_3EO_geoflux=5W.txt (3x Earth Ocean, internal heat flux = 5 W/m2) │  │  │ │  ├─ Spectra │  │  ├─ Adiabatic │  │  │   ├─ Trappist1b_adiabatic_spectra_1EO.txt (1x Earth Ocean) │  │  │   ├─ Trappist1b_adiabatic_spectra_3EO.txt (3x Earth Ocean) │  │  │ │  │  ├─ Converged │  │  │   ├─ Trappist1b_spectra_1EO_geoflux=0W.txt (1x Earth Ocean, internal heat flux = 0 W/m2) │  │  │   ├─ Trappist1b_spectra_1EO_geoflux=5W.txt (1x Earth Ocean, internal heat flux = 5 W/m2) │  │  │   ├─ Trappist1b_spectra_3EO_geoflux=0W.txt (3x Earth Ocean, internal heat flux = 0 W/m2) │  │  │   ├─ Trappist1b_spectra_3EO_geoflux=5W.txt (3x Earth Ocean, internal heat flux = 5 W/m2) │ ├─ Fig4_(net stellar and emission fluxes for a converged and adiabatic profiles) │  ├─profiles_Sun_adia_converged_netfluxes.txt │ ├─ Fig5 (Profiles for different instellations, for each star) │  ├─ T1_profiles.txt │  ├─ Proxima_profiles.txt │  ├─ M3_profiles.txt │  ├─ K5_profiles.txt │  ├─ Sun_profiles.txt │  ├─ F1_profiles.txt │ ├─ Fig6 (Comparison 1D vs 3D) │  ├─ 1D │  │  ├─ exok_1D_PTH_trappist1.txt │  │  ├─ exok_1D_PTH_Proxima.txt │  │  ├─ exok_1D_PTH_M3.txt │  ├─ 3D │     ├─ PT_trappist1_3D.dat │     ├─ PT_Proxima_3D.dat │     ├─ PT_M3_3D.dat │     ├─ HeatingRates_3D_trappist1.dat │     ├─ HeatingRates_3D_trappist1.dat │     ├─ HeatingRates_3D_trappist1.dat │ ├─ Fig7 (Profiles for 2 values of instellation and 3 values of cp) │  ├─ profiles_cp_Sun.txt   │ ├─ Fig8 (Profiles for 3 values of instellation and continuums from 2 version of MT_sCKD) │  ├─ profiles_2continuums_Sun.txt   │ ├─ Fig9 (Profiles as a function of surface pressure and internal heat fluxes) │  ├─ EarlyVenus │  │  ├─ profiles_fig9_EarlyVenus_0.1bar.txt │  │  ├─ profiles_fig9_EarlyVenus_1bar.txt │  │  ├─ profiles_fig9_EarlyVenus_10bar.txt │  │  ├─ profiles_fig9_EarlyVenus_100bar.txt │  │  ├─ profiles_fig9_EarlyVenus_270bar.txt │  ├─ Trappist1 │     ├─ profiles_fig9_Trappist1_0.1bar.txt │     ├─ profiles_fig9_Trappist1_1bar.txt │     ├─ profiles_fig9_Trappist1_10bar.txt │     ├─ profiles_fig9_Trappist1_100bar.txt │     ├─ profiles_fig9_Trappist1_270bar.txt │ ├─ readme.txt

opencc-by-4.0Sep 2023View details →

Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

Introduction A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO2 emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition). The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018). Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues. The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO2 release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively. The pre-prepared input data are given in files, see Table 1 for descriptions.     Table 1. Description of input data files. <table> <tbody> <tr> <td> File </td> <td> Description of data </td> </tr> <tr> <td> basal.areas.csv </td> <td> Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   Values of tree species or group correspond to: 1  Scots pine 2  Norway spruce 3  Broadleaved species </td> </tr> <tr> <td> biomass.csv </td> <td> Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> dead_litter.csv </td> <td> Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> ghgi_litter.csv </td> <td> Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha-1) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   Values of ground correspond to litter deposition environment: above  Above-ground litter below  Below-ground litter </td> </tr> <tr> <td> lognat_decomp.csv </td> <td> Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> logyasso_weather_data.csv </td> <td> Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   </td> </tr> <tr> <td> total_area.csv </td> <td> Areas (ha) of drained peatland forests remaining forest land by region and peat_type.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> weather_data.csv </td> <td> Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> </tbody> </table>   The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023. LUKE Statistics <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.

opencc-by-4.0Dec 2022View details →

Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity

Gridded global potentially affected fraction of species (PAF) in 2050 and 2100 per kg GHG for 3 RCPs (2.6, 4.5,8.5) averaged over all species groups.  Full method description is available in the article: <a href="https://www.sciencedirect.com/science/article/pii/S092134492300294X">Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity - ScienceDirect</a>

publicJun 2022View details →

Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy

Open the record for dataset details and reuse information.

publicJul 2019View details →

Data from: Population analysis reveals genetic structure of an invasive agricultural thrips pest related to invasion of greenhouses and suitable climatic space

Open the record for dataset details and reuse information.

publicDec 2023View details →

Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system

Open the record for dataset details and reuse information.

publicJan 2024View details →

Data from: Promoting success in thin layer sediment placement: effects of sediment grain size and amendments on salt marsh plant growth and greenhouse gas exchange

Open the record for dataset details and reuse information.

openCC (other)Jan 2023View details →

Weather data for the period 2016 to 2022 from the HH1 Greenhouse location at University Farms, Case Western Reserve University

Data from the HH1 weather station at University Farms of Case Western Reserve University include observations from 2016 to 2022. University Farms is located in Hunting Valley, Ohio. The weather station was located N 41.493371, W81.422504. Data include date/time (in 15-minute intervals), air temperature, relative humidity, wind speed, wind gust speed, wind direction, and solar radiation.

openCC (other)Jun 2018View details →

Experimentally evaluating effects of postfire drought on establishment, growth and survival of two widespread western conifer species; lodgepole pine and Douglas-fir (greenhouse portion of experiment)

This research seeks to understand how drought following wildfires affects the establishment of two widespread Rocky Mountain tree species, lodgepole pine and Douglas-fir. In Yellowstone National Park, warming, drying conditions are leading to increased frequency of severe wildfires that kill existing trees and trigger regeneration of the forest. These trends are expected to continue. Many tree species in Yellowstone are well adapted to fire. Yet, early and abundant seedling establishment after fire is critical for forests to recover. Tree seedlings are particularly sensitive to drought, and evidence suggests that sufficiently dry postfire conditions could cause widespread tree-seedling mortality, with ecological consequences that may last decades. This research will determine how seedlings from two tree species respond to drought conditions expected in the mid-21st century and will identify the physiological mechanisms that drive seedling response to dry conditions. The results of this study will advance understanding of how forests in the western United States will respond to environmental change over coming decades and provide useful information for western land managers who are grappling with increased wildfires.

openOpenDec 2015View details →

Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities, Toolik Field Station, North Slope Alaska, Arctic LTER 1991.

Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities.

openOpenDec 2015View details →

Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities Arctic LTER experimental plots, Toolik Field Station, 1992.

Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities. This is the second year of collection data.

openOpenDec 2015View details →

Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities, Toolik Field Station, North Slope Alaska, Arctic LTER 1993.

Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities. This is the third year of collection data.

openCC0Jan 2018View details →

Photoprotective leaf pigments and chlorophyll fluorescence measurements during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species

Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.

openCC0Jan 2018View details →

Gas exchange, dieback, leaf water potential and chlorophyll content during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species

openCustomJan 2020View details →

Gleditsia triacanthos (honey locust) greenhouse experiment

Gleditsia seeds were tested for germination success with moisture and light treatments. Seeds were extracted from the fruit pulp, scarified, and placed in a peat medium and exposed to varying degrees of sunlight and soil moisture.