Find research datasets worth reusing

openCC (other)Jun 2024View details →

Field survey of mangrove regeneration, porewater variables, and light in mangrove forests in Everglades National Park, Florida, USA, July 2020 - August 2022

This dataset package encompasses measurements from field surveys of mangrove regeneration, porewater variables, and light conditions across six mangrove sites in the coastal Everglades. The goal of this project was to quantify mangrove regeneration of seedlings and saplings in mid- and downstream locations within three estuaries in Everglades National Park, Florida, USA. We assessed the effects of porewater variables and light conditions on the observed regeneration patterns. The package includes seven datasets: FCE1268_Porewater: Contains measurements of porewater salinity, sulfide, ammonia, nitrite, orthophosphate, and nitrate at a 30 cm depth. Porewater surveys were conducted biannually from 09-10-2020 to 05-17-2022. See also similar porewater data for Florida Coastal Everglades (FCE) long-term sites in data packages knb-lter-fce.1169 and knb-lter-fce.1171, which contain data for SRS-5 and SRS-6, available in the FCE LTER website's data catalog or the EDI repository. FCE1268_Foliar_Nutrient_Content dataset, collected in August 2022, includes measurements of foliar nutrient content (total carbon, total nitrogen, and total phosphorus) for three mangrove species (A. germinans, L. racemosa, R. mangle) of two life stages—seedlings (height < 1 m) and saplings (height ≥ 1 m and Diameter at Breast Height (DBH) < 2.5 cm). FCE1268_Light contains light intensity (foot-candle) measurements taken at 1-hour intervals from 09-18-2020 to 08-29-2022 at mangrove sites and converted photosynthetic active radiation values from an outdoor mesocosm experiment. FCE1268_Sapling_Density provides biannual count measurements of individuals at the sapling plot level (4 m^-2) within each site from 07-09-2020 to 08-29-2022. FCE1268_Seedling_Density contains biannual count measurements of individuals at the seedling plot level (m^-2) within each site from 07-07-2020 to 08-29-2022. FCE1268_Sapling_Regeneration contains height, crown area, and stem elongation measurements of tagged sapling indiv

openCC (other)Jul 2025View details →

Root productivity of riverine and scrub mangroves from the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, March 2024 - April 2024

Root productivity of riverine and scrub mangroves in the Florida Everglades: Mangrove root productivity in the shallow root zone (0-45 cm depth) was estimated at all Shark River (SRS-4, SRS-5, SRS-6, SRS-7) and Taylor River (TS/Ph-6, TS/Ph-7) sites in March-April 2024. Root productivity was estimated with the ingrowth core technique using the same sampling protocol previously published for the study area (Castañeda-Moya et al. 2011). Ingrowth cores (10.2 cm diameter x 45 cm length) were made of synthetic material (3 mm mesh) and filled with root-free commercial sphagnum peat moss. This material has similar soil properties (i.e., bulk density, organic matter content, total C and N) as mangrove peat in our study sites as previously reported by Castañeda-Moya et al. (2011). Ingrowth cores were installed in holes made out with a PVC coring device (10.2 cm diameter x 45 cm length). At each site, 8 ingrowth cores were deployed vertically into the soil to a depth of 45 cm and retrieved one year later (March-April 2024). After collection, ingrowth cores were processed individually and initially rinsed with water through a 1-mm screen mesh to remove soil particles and peat moss material. Live roots were separated manually based on their buoyancy, turgor, and color (Castañeda-Moya et al. 2011; Cormier et al. 2015; Medina-Calderon et al. 2021). Live roots were further sorted into three size diameter classes including fine (<2 mm), small (2-5 mm), and coarse (5-20 mm). Roots greater than 20 mm in diameter were not included in this study due to sampling limitations (i.e., core area). All root samples were oven-dried at 60°C to a constant mass and weighed. Root growth within each ingrowth core following one year of incubation was used to estimate annual root productivity (g m⁻² yr⁻¹) in the shallow root zone across all mangrove sites. All data collection and processing were funded by FCE-LTER. Data collection is complete. References: Castañeda-Moya, E., R.R. Twilley, V.H. Rivera-

openCC (other)Feb 2026View details →

Mangrove soil biogeochemistry and geomorphology data from Biscayne National Park, Florida, USA, 2011 - 2024

We quantified long-term changes in tidal hydrology and surface soil elevation (2011-2024) across two representative fringe mangrove forest sites (BISC-1, BISC-2) in Biscayne National Park (Florida, USA). We measured the spatiotemporal variation of monthly wrack deposition, litter breakdown rates, soil organic carbon, and stable isotope δ13C and δ15N content along landward transects in Biscayne National Park (Florida, USA) from 2022 to 2024. We surveyed marine wrack deposition biovolume monthly using a quadrat in plots along our transects. At each marine wrack survey plot, we collected soil cores seasonally to measure soil physicochemistry. Finally, we deployed leaf litter decomposition mesh bags with Rhizophora mangle leaf litter, Thalassia testudinum leaf litter, and teabag standards to quantify breakdown rates on the soil surface of each marine wrack survey plot. Data collection is complete.

openCC (other)Dec 2025View details →

Major Ion Concentrations in Surface Water Collected from Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, December 2003 – December 2015

This package includes data of concentrations of sodium, potassium, magnesium, calcium, chloride, and sulfate in surface water samples collected from Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) Program sites in Taylor Slough. These sites are TS/Ph1a (2003-2013), TS/Ph2 (2003-2012), and TS/Ph3 (2004-2015). Analyzed samples include composite samples, rainfall samples, and grab samples. Composite samples represent water collected over the course of 3 days by autosamplers programmed to draw 250 mL every 18 hours. Rainfall samples represent water collected by the autosamplers when a threshold of = 2.5 cm of rain per hour is passed. A 500 mL sample is collected 30 minutes after meeting the threshold. Composite and rainfall samples are retrieved every 3-4 weeks and returned to the Florida International University (FIU) Modesto A. Maidique campus. A grab sample is collected at each site during these visits. Cation and anion analysis were completed using ion chromatography on a Dionex DX-120. Sample preparation and analysis for major ions were completed in the Hydrogeology laboratory at FIU. This dataset is completed.

openCC (other)Dec 2022View details →

Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.

To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).

opencc-by-4.0Jan 2020View details →

Sila National Park - 3D Point cloud data

This dataset contains 3 types of data. <ul> <li>GPS data (the ones starting with "GPS") of sampling plot centers collected with a Trimble GPS and post processed to ensure positioning errors lower than 2 meters.</li> <li>TLS data, (the ones starting with "ID_"): such data were collected in the end of August 2019 with a mobile terrestrial laser scanner (mobile ZEB TLS) in a squared area of approximatively 30x30m. Data have been normalized using TreeLS package in R.</li> <li>ALS data collected in the end of July 2019. For the entire study area, we upload 2 different ALS data: "merged.las" is the original point cloud; "myLas_norm_lt22.las" is the normalised point cloud, cut at 22 meters from the ground in order to perform specific analysis (i.e. paper under submission).</li> </ul> Data collection was founded by the AGRIDIGIT Selvicoltura project.

opencc-by-4.0Dec 2019View details →

Annotated checklist of the beetles of beech forests in Matese National Park, Central Italy

The checklist contains 165 species of beetles which belong to 37 families. The species were collected during a field study carried out in the year 2018 and aimed at describing the community of beetles. The collection methods consisted of window flight traps. The study activities were carried out in four distinct beech forest stands based on their altitude (High and Low) and exposure (South and North) and located in the Italian Central Apennines. The sites are included in the Natura 2000 site IT 7222287 “La Gallinola - Monte Miletto - Monti del Matese” and Matese National Park. The checklist is annotated with information on the taxonomy of the species (order and family), number of individuals, geographic position, habitat type (following EUNIS habitat classification 2017), sampling protocol, collector name, specialist name, IUCN Red List categories of the saproxylic species (Carpaneto et al. 2015).  The terms used for the dataset fields follows the Darwin Core Maintenance Group. 2020. List of Darwin Core terms. Biodiversity Information Standards (TDWG). <a href="https://dwc.tdwg.org/list/">https://dwc.tdwg.org/list/</a> The discovery of a new species of beetle (Elateridae) for the Italian fauna was based on this dataset (Parisi et al., 2020). The harmonization of the dataset to the point of view of taxa, authorship, LSID and the massive upgrading of the related identifiers in Zenodo record was performed by the use of R script using respectively dplyr, taxize (Chamberlain and Szöcs, 2013) and zen4r (Blondel and Barde, 2020) packages.

opencc-by-4.0Dec 2019View details →

Annotated checklist of the beetles of three beech forests in Gran Sasso National Park, Central Italy

The checklist contains 163 species of beetles which belong to 36 families. The species were collected during a field study carried out in the years 2013 and 2016 and aimed at describing the community of beetles. The collection methods consisted of window flight traps and emergence traps. The study area included 3 beech forest sites, named Prati di Tivo (42.5096 N, 13.5679 E), Venacquaro (42.4988 N, 13.5139 E) and Incodara (42.5123 N, 13.4735 E) located in the Italian Central Apennines. The sites are included in the Natura 2000 site IT7110202 “Gran Sasso”. The checklist is annotated with information on the taxonomy of the species (order and family), number of individuals, locality, habitat type (following EUNIS habitat classification 2017), sampling protocol, collector name, specialist name, IUCN Red List categories of the saproxylic species (Carpaneto et al. 2015).  The terms used for the dataset fields follows the Darwin Core Maintenance Group. 2020. List of Darwin Core terms. Biodiversity Information Standards (TDWG). <a href="https://dwc.tdwg.org/list/">https://dwc.tdwg.org/list/</a> Investigations on stand structure and forest biodiversity (Sabatini et al. 2016) and faunistic analysis (Zanetti and Parisi 2019) have been based on this dataset. The harmonization of the dataset to the point of view of taxa, authorship, LSID and the massive upgrading of the related identifiers in Zenodo record was performed by the use of R script using respectively dplyr, taxize (Chamberlain and Szöcs, 2013) and zen4r (Blondel and Barde, 2020) packages.

opencc-by-4.0Feb 2021View details →

Reactive nitrogen fluxes over peatland (Bourtanger Moor) and forest (Bavarian Forest National Park) using micrometeorological measurement techniques

Within the framework of the research projects NITROSPHERE and FORESTFLUX, field campaigns were carried out to investigate the biosphere-atmosphere exchange of reactive nitrogen compounds. We applied novel fast-response instruments in eddy-covariance setups for continuous determination of surface ammonia (NH3) and total reactive nitrogen (\(\Sigma\)Nr) fluxes using two different analytical devices. While high-frequency measurements of ammonia were measured with a quantum cascade laser absorption spectrometer (QCL), a custom-built converter called TRANC coupled to a chemiluminescence detector was used for the determination of total reactive nitrogen. High-resolution data of surface-atmosphere fluxes of reactive compounds are still scarce, but highly desired for testing and validating local inferential and larger scale models. We provide access to campaign data including concentrations, fluxes and ancillary measurements of meteorological data. Campaigns were conducted in natural (forest) and semi-natural (peatland) ecosystem types. The published datasets stress the importance of recent advancements in laser spectrometry and help improve our understanding of the temporal variability of surface-atmosphere exchange in different ecosystems, thereby providing validation opportunities for inferential models simulating the exchange of reactive nitrogen.

opencc-by-4.0Apr 2022View details →

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2020 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance. During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020). Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.

opencc-by-4.0Apr 2022View details →

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2021 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance. During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020). Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.

opencc-by-4.0Jun 2022View details →

Orangutan habitat survey in Sebangau National Park, Central Kalimantan, Indonesia

This dataset is used to initialise BORNEO (arBOReal aNimal movEment mOdel), as a part of publication entitled: Assessing the impact of forest structure disturbances on the arboreal movement oforangutans - an agent-based modelling approach. The article manuscript is being prepared to be submitted to Frontiers in Ecology and Evolution Data collection The data is collected in Sebangau, Central Kalimantan, Indonesia. Two 1-ha plots were established, each in unburned and burned forest.

opencc-by-4.0Apr 2024View details →

Chacma baboon male-male greeting dataset, Gorongosa National Park, Mozambique

See README file for more complete information Overview: This dataset provides presence/absence data for each of 55 observations from Gorongosa National Park. Each observation is one male-male greeting. Data was first collected using BORIS (Friard and Gamba, 2016) following an ethogram (DOI:10.5281/zenodo.7314291), then cleaned using Python version 3.8.5 and R version 4.3.1 to format as a presence/absence matrix. Some of these observations (with observation IDs ending in "_greet[xx]" also appear in the more general approach dataset found under the following DOI 10.5281/zenodo.10339023. These observations are part of both datasets as they qualify both as male-male greetings and also as approaches from outside 5 m to within 2 m.  Study species: Papio ursinus griseipes (chacma baboon) Dates of data collection: Videos in Gorongosa National Park were filmed 2018-10 to 2018-11 and 2019-07 to 2019-11.  Geographic location of data collection: Gorongosa National Park, Mozambique Video coding software: BORIS (Friard and Gamba, 2016) Recommended citation in BibTex form: @ELECTRONIC{Muschinski2024,   author = {Muschinski, Jana and Carvalho, Susana},   year = {2024},   title = {Chacma baboon male-male greeting dataset, {G}orongosa {N}ational {P}ark, {M}ozambique},   doi = {10.5281/zenodo.11097938},   owner = {Paleo-Primate Project, Gorongosa National Park},   organization = {Paleo-Primate Project, Gorongosa National Park} }

opencc-by-4.0Aug 2024View details →

Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks

China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making. This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution. The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1. The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring. The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs. This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions. The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park: <ul> <li>NTLNP-RSEI.rar: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li>HTRNP-RSEI.rar: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li>WNP-RSEI.rar: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li>SNP-RSEI.rar: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li>GPNP-RSEI.rar: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period. The details of the data are as follows: <ul> <li>Data Format: GeoTiff</li> <li>Pixel Values: Represent RSEI, ranging from 0 to 1, with no units.</li> <li>Compatibility: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li>Data Quality: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas. For using the data and code provided in this dataset, please cite the following paper: Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. Ecological Indicators, 170, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a> The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is: <a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a>

openCC (other)Feb 2022View details →

Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming.

Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuels feedbacks on burn severity. For a about 1,450 km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature ( greater than 125-year-old) and also reburned young (~30-year-old) subalpine forest, we then asked: (1) How do pre-fire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation (R2 from 0.65-0.95). Young post-fire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme (R2LMM(m) = 0.27) rather than moderate (R2LMM(m) = 0.15) fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering post-fire forests can limit fire spread and severity for a period of time, our resu

openCC (other)Feb 2022View details →

Virgin Islands National Park: Coral Reef: Population Dynamics: Landscape-scale Variation in Scleractinian Corals

This study provides a landscape-scale context to a decadal-scale analysis of community structure on shallow reefs along 4 km of the south shore of St. John, US Virgin Islands. By focusing on 12-14 sites along ~100 km of the shores of St. John and St. Thomas, surveys conducted in 2011 were used to contrast: (1) a local-scale with a landscape-scale analysis on two islands, (2) reefs around St. John and St. Thomas, and (3) reefs on north and south shores. Reefs were censused using photoquadrats that were analyzed for percentage cover first by functional groups (coral, macraolagae and CTB), and then by coral genus. In general, among-site variation for the coarse-resolution analysis eclipsed shore and island effects, but the fine-resolution analysis revealed strong site-specific differences for multiple coral genera that could be the product of priority effects in community succession. Over the next decade these differences probably will create unique community trajectories at each site.

openCC (other)Sep 2023View details →

Limnological data for 17 mountain lakes in Banff and Yoho National Parks (Canadian Rocky Mountains) from 2015 to 2022

From 2015 to 2022, mid-summer vertical profiles of temperature, chlorophyll a fluorescence, turbidity, and fDOM were collected in a set of 17 lakes in Banff and Yoho National Parks, Canada. These lakes are located across montane, sub-alpine and alpine ecoregions and they vary widely in elevation (1300-2423 m a.s.l.), surface area (1.5-116 ha) and maximum depth (2.4-39.2 m). Eight of the lakes receive surface and/or groundwater hydrologic inputs from glaciers within the catchment, and the other nine lakes are not glacially-fed. Vertical profiles were collected in each lake within one or two days of an index sampling date between late July and early August using an Exo2 vertical profiling sonde. Measurements were taken at 1 s intervals as the sonde was lowered slowly through the water column, and then averaged over 0.5 m depth intervals. In addition, attenuation rates were estimated for 305 nm, 320 nm, and 380 nm, and PAR (400-700 nm) as the slopes of log-linear regressions of irradiance vs. depth. Downwelling irradiance measured with a Biospherical Instruments underwater radiometer. Vertical Profile Data are contained in Can_Rocky_Mtn_Lakes_Profiles.csv. Attenuation rates are contained in Can_Rocky_Mtn_Lakes_Kd.csv. Information about study lakes is contained in Can_Rocky_Mtn_Lakes_Site_Information.csv.

openCC (other)Sep 2023View details →

COI and 18S metabarcoding data from Hidden Lake (Banff National Park, Canada) over two rotenone applications between 2018 and 2020.

Water samples were taken in Hidden Lake at five different time points around two rotenone applications: (i) five weeks prior to the first rotenone application, on July 12 2018; (ii) approximately three weeks after the first application of rotenone, on 7 September 2018; (iii) approximately 10 months after the first rotenone application, on 10 July 2019; (iv) four weeks following the final treatment of rotenone on the 17 September 2019; and (v) one year after the final rotenone treatment, on 19 August 2020. For each time point there is a pelagic, a littoral and a profundal sample. COI and 18S metabarcoding methods were used to produce community data. The objective of this study was to assess the non-target effect of rotenone application (in summer 2018 and 2019) on aquatic communities (i.e. phytoplankton, fungi, zooplankton and benthic macroinvertebrates).

openCC (other)Sep 2023View details →

Brook trout (Salvelinus fontinalis) cyt b qPCR data from Hidden Lake (Banff National Park, Canada) over two rotenone applications between 2018 and 2020.

Water samples were taken in Hidden Lake at five different time points around two rotenone applications: (i) five weeks prior to the first rotenone application, on July 12 2018; (ii) approximately three weeks after the first application of rotenone, on 7 September 2018; (iii) approximately 10 months after the first rotenone application, on 10 July 2019; and (iv) one year after the final rotenone treatment, on 19 August 2020. For each time point, four pelagic and four littoral water samples were taken from Hidden Lake, as well as 8 to 13 water samples from Hidden Creek and Coral Creek for a total of 16 to 21 samples per time point. Quantitative PCR (qPCR) method was used to produce brook trout (Salvelinus fontinalis) cytochrome b copy number for each sample. The objective of this study was use eDNA to assess the efficacy of invasive brook trout removal using rotenone.