• Energy Research
• Open Access close
• Closed Access close
• Embargo close
• CA close

GENERAL INFORMATION 1. Title of Dataset: ThermalManipulation_Data 2. Date of data collection : Main text data: 2019-06-19 Supplemental Data: 2021-03-20 3. Geographic location of data collection Main article data: April Point, Quadra Island, British Columbia, Canada (50.064067, -125.236816) Supplemental Data: Spanish Banks beach, Vancouver, British Columbia, Canada (49.278124, -123.222027) ---------------------------------------------------------------------------------------------------------------- DATA & FILE OVERVIEW 1. File List: Main text: - 01_Quadra_heatingData.csv: data was collected to evaluate the initial tidepool heating manipulation trial. The data contains information on abiotic parameters for each treatment condition. - 02_Quadra_coolingData.csv : data was collected to evaluate the tidepool cooling temperature manipulation trial. The data contains information on abiotic parameters for each treatment condition throughout the trial. Supporting Materials: - 03_SupplementalData_Trial1_HOBO.csv: data was collected to assess how pool temperatures and heating by the SAUTE may vary between containers of similar volume but different dimensions. This dataset provides high temporal resolution temperature measurements. - 04_SupplementalData_Trial1_SpotMeas.csv: data was collected to assess how pool temperatures and heating by the SAUTE may vary between containers of similar volume but different dimensions. This dataset contains spot measurements at a variety of locations (see diagrams in supplement of publication) throughout each container to better understand patterns of thermal variation within a container. - 05_SupplementalData_Trial2_HOBO.csv: data was collected to evaluate the ability of the SAUTE to heat a large volume of water (100L). HOBO data loggers were placed at various locations (see diagrams in supplement of publication) throughout each container. - 06_SupplementalData_Trial2_SpotMeas: data was collected to evaluate the ability of the SAUTE to heat a large volume of water (100L). Spot measurements were taken at various locations (see diagrams in supplement of publication) throughout each container to assess thermal variation within a container. ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [01_Quadra_heatingData.csv] 1. Number of variables: 8 2. Number of cases/rows: 135 3. Variable List: - pool_ID: unique identifier for each pool - treatment: level of treatment condition (Ambient/Control/Heated) - time: time of measurement (PST, 24hr format) - temperature: water temperature (degrees celsius) - salinity: water salinity (psu) - pH: water pH - DO_mg_L : Dissolved Oxygen (mg/L) measured in a subset of pools and timepoints - DO_Percent : Dissolved Oxygen (%) measured in a subset of pools and timepoints 4. Missing Values: NA values indicate missing values. Data was not collected for given parameter(s) at these timepoints. ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [02_Quadra_coolingData.csv] 1. Number of variables: 5 2. Number of cases/rows: 64 3. Variable List: - pool_ID: unique identifier for each pool - treatment: level of treatment condition (Cooled/Control) - time: time of measurement (PST, 24hr format) - temperature: water temperature (degrees celsius) - salinity: water salinity (psu) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [03_SupplementalData_Trial1_HOBO.csv] 1. Number of variables: 5 2. Number of cases/rows: 1600 3. Variable List: - Date-Time (PST): Date and time (PST & 24hr format) of temperature measurement - heating_treatment: level of treatment condition (Control/Heated) - container_type: type of experimental container used (bucket/tray) - container_ID: Identifier within each heating_treatment*container_type combination - temperature: water temperature (degrees celsius) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [04_SupplementalData_Trial1_SpotMeas.csv] 1. Number of variables: 6 2. Number of cases/rows: 486 3. Variable List: - container_type: type of experimental container used (bucket/tray) - container_ID: Identifier within each heating_treatment*container_type combination - heating_treatment:level of treatment condition (Control/Heated) - measurement_location:measurement locations within a container - corresponding diagram of locations in the supplemental materials for the accompanying article (a,b,c,d,e) - time: time of measurement (PST, 24hr format) - temperature: water temperature (degrees celsius) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [05_SupplementalData_Trial2_HOBO.csv] 1. Number of variables: 5 2. Number of cases/rows: 1190 3. Variable List: - Date-Time (PST): Date and time (PST & 24hr format) of temperature measurement - heating_treatment: level of treatment condition (Control/Heated) - logger_depth: relative depth of data logger within each experimental container (shallow/bottom) - logger_location: relative logger location within each depth (corner/centre) * Note: Control treatment does not have data for the [shallow, corner] location * - temperature: water temperature (degrees celsius) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [06_SupplementalData_Trial2_SpotMeas] 1. Number of variables: 6 2. Number of cases/rows: 20 3. Variable List: - container_type: type of experimental container used (tub) - heating_treatment:level of treatment condition (Control/Heated) - measurement_location: measurement location within container based on cardinal directions (NE, NW, centre, SE, SW) - measurement_depth: relative depth of temperature measurement within each experimental container (shallow/ bottom) - temperature: water temperature (degrees celsius) - time: time of measurement (PST, 24hr format) There is a growing need to better understand the potential impacts of altered thermal regimes on biodiversity and ecosystem function as mean temperatures, and the likelihood of extreme temperatures, continue to increase. One valuable approach to identify mechanisms and pathways of thermally-driven change at the community level is through the manipulation of temperature in the field. However, where methods exist, they are often costly or unable to produce ecologically relevant changes in temperature. Here, we present a low cost, easily assembled, and readily customizable thermal manipulation system for tide pools or other small bodies of water – the Seaside Array for Understanding Thermal Effects (SAUTE) – and demonstrate its ability to effectively alter the temperature in tide pools. During our three-hour heating manipulation, heated pools reached temperatures 4°C warmer than unmanipulated pools. During the cooling manipulation, cooled pools remained on average 1.8°C cooler than control pools. The novel SAUTE system can be used to alter the temperature of tide pools in situ. Further, it could be modified to heat other environments such as freshwater vernal pools and settlement tiles in a realistic and meaningful manner, serving as a useful tool to test questions surrounding the relationship between climate warming, thermal variability, and ecological processes in natural aquatic communities. Please see the linked publication and supporting materials for full methodological details.

Please cite as: Olivia Graham, Tiffany Stephens, Brendan Rappazzo, Corinne Klohmann, Sukanya Dayal, Emily Adamczyk, Angeleen Olson, Margot Hessing-Lewis, Morgan Eisenlord, Bo Yang, Colleen Burge, Carla Gomes, Drew Harvell. (2022) Data and code from: Deeper habitats and cooler temperatures moderate a climate-driven disease in an essential marine habitat [dataset] Cornell University eCommons Repository. https://doi.org/10.7298/6ybh-w566 ; These files contain data and R code supporting all results reported in Graham et al. "Deeper habitats and cooler temperatures moderate a climate-driven disease in an essential marine habitat." In Graham et al., we found: Eelgrass creates critical coastal habitats worldwide and fulfills essential ecosystem functions as a foundation seagrass. Warming and disease threaten eelgrass meadows with mass mortalities and cascading ecological impacts, even in pristine locations. Although deeper, subtidal meadows are valuable fish nursery grounds and may also provide refuge from the climate-fueled seagrass wasting disease, nothing is known about differences in disease levels across remote locations in northern latitudes and between tidal zones (intertidal and subtidal meadows). From cross-boundary surveys on 5,761 eelgrass leaves from Alaska to Washington assisted with a machine-language algorithm, we measured outbreak conditions with average disease prevalence over 66% in intertidal and 50% in subtidal. In field surveys, disease was consistently lower in subtidal compared to adjacent intertidal meadows; remotely-sensed temperatures revealed significant associations between spring temperature anomalies and disease. While new studies show links between warm temperature anomalies and increased disease, our work detects beneficial effects of cooling in colder water anomalies. Disease was reduced in all regions except Puget Sound in the cooler summer of 2017. Pooled across both years, predicted disease prevalence was nearly 40% lower for subtidal than intertidal leaves, but in both tidal zones, ...

Transmitted solar radiance was measured using an ARC (Advanced-Radiance-Collector) RAMSES hyper-spectral radiometer (TriOS) mounted on the ROV during the ARTofMELT2023 expedition in May and June 2023 and normalized by the incident solar irradiance as measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship. All times are given in Universal Coordinated Time (UTC).

Rapid, ongoing permafrost thaw of peatlands in the discontinuous permafrost zone is exposing a globally significant store of soil carbon (C) to microbial processes. Mineralisation and release of this peat C to the atmosphere as greenhouse gases is a potentially important feedback to climate change. Here we investigated the effects of permafrost thaw on peat C at a peatland complex in western Canada. We collected 15 complete peat cores (between 2.7 abd 4.5 m deep) along four chronosequences, from elevated permafrost plateaus to saturated thermokarst bogs that thawed up to 600 years ago. The peat cores were analysed for peat C storage and peat quality, as indicated by decomposition proxies (FTIR and C/N ratios) and potential decomposability using a 200-day aerobic incubation. Our results suggest net C loss following thaw, with average total peat C stocks decreasing by ~19.3 +/- 7.2 kg C m-2 over <600 years (~13% loss). Average post-thaw accumulation of new peat at the surface over the same period was ~13.1 +/- 2.5 kg C m-2. We estimate ~19% (+/- 5.8%) of deep peat (>40 cm below surface) C is lost following thaw (average 26 +/- 7.9 kg C m-2 over <600 years). Our FTIR analysis shows peat below the thaw transition in thermokarst bogs is slightly more decomposed than peat of a similar type and age in permafrost plateaus, but we found no significant changes to the quality or lability of deeper peat across the chronosequences. Our incubation results also showed no increase in C mineralisation of deep peat across the chronosequences. While these limited changes in peat quality in deeper peat following permafrost thaw highlight uncertainty in the exact mechanisms and processes for C loss, our analysis of peat C stocks shows large C losses following permafrost thaw in peatlands in western Canada.

Transmitted solar radiance was measured using an ARC (Advanced-Radiance-Collector) RAMSES hyper-spectral radiometer (TriOS) mounted on the ROV during the ARTofMELT2023 expedition in May and June 2023. All times are given in Universal Coordinated Time (UTC).

This repository contains the raw data of the inputs and results presented in the paper "Electrification of light-duty vehicle fleet alone will not meet mitigation targets" published in Nature Climate Change (2020) by Alexandre Milovanoff, I. Daniel Posen, and Heather L. MacLean (Department of Civil & Mineral Engineering, University of Toronto).

Climate-associated decline of body condition in a fossorial salamander Abstract Temperate ectotherms have responded to recent environmental change, likely due to the direct and indirect effects of temperature on key life-cycle events. Yet, a substantial number of ectotherms are fossorial, spending the vast majority of their lives in subterranean microhabitats that are assumed to be buffered against environmental change. Here we examine whether seasonal climatic conditions influence body condition (a measure of general health and vigor), reproductive output, and breeding phenology in a northern population of fossorial salamander (Spotted Salamander,Ambystoma maculatum). We found that breeding body condition declined over a 12 year monitoring period (2008–2019) with warmer summer and autumn temperatures at least partly responsible for the observed decline in body condition. Our findings are consistent with the hypothesis that elevated metabolism drives the negative association between temperature and condition. Population-level reproduction, assessed via egg mass counts, showed high interannual variation and was weakly influenced by autumn temperatures. Salamander breeding phenology was strongly correlated with lake ice-melt but showed no long-term temporal trend (1986–2019). Climatic warming in the region, which has been and is forecasted to be strongest in the summer and autumn, is predicted to lead to a 5 to 27% decline in salamander body condition under realistic near-future climate scenarios. Although the subterranean environment offers a thermal buffer, the observed decline in condition and relatively strong effect of summer temperature on body condition suggest that fossorial salamanders are sensitive to the effects of a warming climate. Given the diversity of fossorial taxa, heightened attention to the vulnerability of subterranean microhabitat refugia and their inhabitants is warranted amid global climatic change. The dataset and corresponding R script are split into five parts, consistent with the presentation of Methods/Results in Moldowan et al. Part 1 of 5: Body condition data and analysis files 2008.2019.female.SMI.CONSTANTSVL.csv 2008.2019.male.SMI.CONSTANTSVL.csv 2009.2019.female.SMI.CONSTANTSVL.csv 2009.2019.male.SMI.CONSTANTSVL.csv BodyCondition.TimeSeries.WeightedRegression.csv SMAregression.Female.2009.2019.R SMAregression.Male.2009.2019.R ModelSel.Avrg.Forecast.AutoCor.FemaleBodyCondition.climate.R ModelSel.Avrg.Forecast.AutoCor.MaleBodyCondition.climate.R WeightedRegression.BodyCondition.TimeSeries.R YearEffects-Njal_PDM update (20 March 2021) Part 2 of 5: Forecast body condition under climate change files 2009.2019.male.SMI.CONSTANTSVL.csv (as above in Part 1) HeatMap.Forecast.MaleSMI.04 Feb 2021.R Part 3 of 5: Reproductive output (egg mass) data and analysis files 2009.2019.EggCount.Climate.csv ReproductiveOutput.climate.R Part 4 of 5: Breeding phenology data and analysis files 2008.2019.BreedingPhenology.Climate.csv Opeongo.Two Rivers.Bat.IceOff.csv BreedingPhenology.climate.R Part 5 of 5: Temperature dataloggers and salamander metabolic rate estimation files HOBO_Bat_Lake_Underground_Temperatures.csv WhitfordHutchison1967Data.csv WhitfordHutchison1967DataExplainer.xlsx Metabolic Rate Prediction_PDM, 21 Feb 2021.R

Global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Before management and stakeholders can make informed choices on potential mitigations, it is essential to understand how the hydropower reservoir ecosystems respond to water level regulation (WLR) impacts that are likely modified by the reservoirs' abiotic and biotic characteristics. Yet, most reservoir studies have been case-specific, which hampers large-scale planning, evaluation and mitigation actions across various reservoir ecosystems. Here, we investigated how the effect of the magnitude, frequency and duration of WLR on fish populations varies along environmental gradients. We used biomass, density, size, condition and maturation of brown trout (Salmo trutta L.) in Norwegian hydropower reservoirs as a measure of ecosystem response, and tested for interacting effects of WLR and lake morphometry, climatic conditions and fish community structure. Our results showed that environmental drivers modified the responses of brown trout populations to different WLR patterns. Specifically, brown trout biomass and density increased with WLR magnitude particularly in large and complex-shaped reservoirs, but the positive relationships were only evident in reservoirs with no other fish species. Moreover, increasing WLR frequency was associated with increased brown trout density but decreased condition of individuals within the populations. WLR duration had no significant impacts on brown trout, and the mean weight and maturation length of brown trout showed no significant response to any WLR metrics. Our study demonstrates that local environmental characteristics and the biotic community strongly modify the hydropower-induced WLR impacts on reservoir fishes and ecosystems, and that there are no one-size-fits-all solutions to mitigate environmental impacts. This knowledge is vital for sustainable planning, management and mitigation of hydropower operations that need to meet the increasing worldwide demand for both renewable energy and ecosystem services delivered by freshwaters. Data of environmental characteristics and brown trout populations in 102 Norwegian hydropower reservoirsThe data contains field-collected data of brown trout populations in 102 Norwegian reservoirs with variable environmental characteristics. The brown trout data (i.e. response variables) include estimates of: "Biomass" (grams of fish per 100m2 net per night); "Density" (number of fish per 100m2 net per night); "Mean weight" (mean wet mass in grams); "Mean condition" (mean Fulton's condition factor); and "Mean maturity length" (mean total length of mature females in millimeters). All abbreviations for different variables (columns) are explained in the paper. Many reservoirs ("Lake") have various names, some including Norwegian letters (æ, ø & å). Hence, we recommend to use coordinate data (EPSG:4326; "decimalLongitude" and "decimalLatitude") and Norwegian national lake ID numbers ("Lake_nr"; managed by the Norwegian Water Resources and Energy Directorate; www.nve.no) to locate the reservoirs. The variables "Year", "Month" and "Day" refer to times when survey fishing was conducted. Lake morphometry data ("A"=surface area, "SD"=shoreline development) is obtained from NVE database. The lake climatic and catchment data ("T"=mean July air temperature, "NDVI"= Normalized Difference Vegetation Index, and "SL"=terrain slope) is obtained and measured as described by Finstad et al. (2014; DOI: 10.1111/ele.12201). Other abbreviations include: "FC"=presence of other fish species (1=absent, 2=present); "GS"=gillnet series (1=Nordic, 2=Jensen); and "ST"=brown trout stocking (0=no stocking, 1=stocking). The water level regulation (WLR) metrics include: ): "WLR_magnitude"= maximum regulation amplitude; "WLR_frequency"=relative proportion of weeks with a sudden rise or drop in water level; and "WLR_duration"=the relative proportion of weeks with exceptionally low water levels.Data-in_doi.org-10.1016-j.scitotenv.2017.10.268.xlsx