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153 results for “Long-term dynamic”
MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Corals, ongoing since 2005
This dataset contains the percentage cover of the stony corals (Scleractinia) and other major groups analyzed from 0.5 x 0.5 m photographic quadrats in several reef habitats at the Moorea Coral Reef LTER, French Polynesia. This survey has been repeated annually in April since 2005. There are two tables available, providing different views of the same data: a long table having all values in one column and a wide table having a separate column for each dependent variable. Functional groups (i.e., dependent variables) counted are: Scleractinian Corals (by genus where appropriate, see methods), Macroalgae, Crustose Coralline Algae / Bare Space, Soft Corals, Hydrocorals (Millepora), Algal Turf, and Sand. The coral community was sampled photographically in all habitats surrounding the island: Fringing Reef, Lagoon (Backreef), and Outer Reef (Forereef.) The sampling regime consists of a repeated-measures protocol in each habitat and is structured by habitat to allow a statistical contrast of sites, shores, times, and in the case of the outer reef, depths. Detailed methods are available in the protocols section.
MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Fishes, ongoing since 2005
These data describe the estimated species abundances and individual sizes of fishes surveyed in late July or early August of each year by researchers associated with the Moorea Coral Reef Long Term Ecological Research site. This study began in 2005, and the dataset is updated annually. Divers using SCUBA estimate the number and total length (total body length to the greatest precision possible) of all mobile and semi-cryptic fishes observed on four 50m long transects within each of the three principal habitat types, fore reef, back reef, and fringing reef, found around Moorea. These three habitats are each surveyed at six permanently marked sites, two sites on each of Moorea's three shores, yielding a total of 18 unique habitats (3) by location (6) combinations and 72 transects. Estimated total lengths are converted to estimates of species biomass using published length-weight relationships for each species. Data from the initial survey conducted in 2005 are presented in a separate data table as the protocol used in 2005 differed from the standard protocol adopted in 2006, and no estimates of fish body lengths were made in 2005. These data are a component of the MCR LTER core time series program and provide insight into the spatial and temporal patterns of reef fish abundance and biomass around the island of Moorea, French Polynesia.
MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Other Benthic Invertebrates, ongoing since 2005
The data presented here are the abundances of the major invertebrate herbivores and corallivores on Moorea coral reefs. Abundances are estimated in 4 fixed quadrats along 5 permanent transects at each of 4 habitats at 2 sites on each of the 3 shores of Moorea each year. Counts are made in one-meter-squared quadrats.
MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Benthic Algae and Other Community Components, ongoing since 2005
Coral reefs are comprised of scleractinian corals and many other benthic organims. The sampling described here quantifies the relative abundances of corals (aggregate abundance) and the other major benthic components including algal turfs, macroalgae, crustose corallines, and other sessile invertebrates. Abundance is estimated yearly at each of 6 sites (2 per shore) around the island. At each site, and in each of 4 habitats (fringing reef, backreef, forereef 10-m depth, forereef 17-m depth), 5 permanent 10-m long transects have been established and abundance estimates are made at fixed positions along each transect (n=10, 0.25 m2 quadrats per transect) allowing a repeated measures statistical analysis for the detection of temporal trends.
Long-Term Core Site Grasshopper Dynamics for the Sevilleta National Wildlife Refuge, New Mexico
Grasshoppers are important animals in semi-arid environments, both as herbivores and as food resources for higher level consumer animals. Grasshoppers tend to be numerous and speciose in semi-arid environments, especially desert grasslands, where they range from environmental specialists to environmental generalists. Grasshopper populations tend to change considerably from year to year, often in response to annual variation in rainfall and plant production. The purpose of this study was to monitor grasshopper species composition and abundance over large temporal and spatial dimentions which include black grama grassland, blue grama grassland, creosotebush shrubland, and pinyon/juniper woodland environments at the Sevilleta, in relation to seasonal and annual variation in precipitation and plant production. Data were collected for all individual species to provide information on community dynamics as well as population dynamics, starting in 1992 and continuing to the present. The working research hypothesis for this study was that grasshopper populations in all environments will correlate positively to seasonal and annual variation in precipitation and plant production. Spring grasshopper populations will be especially high during El Nino years, and late summer populations especially high during La Nina years.
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Data from: Structure and dynamics of secondary and mature rainforests: insights from South Asian long-term monitoring plots
<p><strong>1) DESCRIPTION </strong></p> <p>The dataset contains annual woody stems (shrubs and trees) census data collected from two long-term ecological monitoring plots spanning one hectare each in the Anamalai Hills of the Southern Western Ghats, India. These two plots represent one situated in a mature forest located within relatively undisturbed rainforest of the Anamalai Tiger Reserve (ATR) and one in secondary forest on the Valparai Plateau, respectively. Both plots have been censused and measured from 2017 to 2022 following the standardized protocol (RAINFOR-GEM, Marthews et al. 2014).</p> <p><br><strong>2) CONTACTS</strong></p> <p>CONTACT #1<br>1. Name: Akhil Murali<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 82812 97441<br>4. Email address: akhil@ncf-india.org<br>5. ORCID: 0000-0001-6149-6458</p> <p>CONTACT #2<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: srini@ncf-india.org<br>5. ORCID: 0000-0001-7323-6653 </p> <p>CONTACT #3<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: kshama@ncf-india.org<br>5. ORCID: 000-0002-6190-2687</p> <p>CONTACT #4 <br>1. Name: Jayashree Ratnam <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001 <br>4. Email address: jratnam@ncbs.res.in <br>5. ORCID: 0000-0002-6568-8374</p> <p>CONTACT #5<br>1. Name: Mahesh Sankaran <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001<br>4. Email address: mahesh@ncbs.res.in <br>5. ORCID: 0000-0002-1661-6542</p> <p>CONTACT #6<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: 0000-0001-9708-4826</p> <p>CONTACT #7<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: 0000-0002-1347-3953</p> <p>CONTACT #8<br>1. Name: Anand M Osuri <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: aosuri@ncf-india.org <br>5. ORCID: 0000-0001-9909-5633</p> <p><br><strong>3) GEOGRAPHIC COVERAGE and SITE DESCRIPTION</strong></p> <p>a) Site type: : Tropical Forest<br>b) Geography: : Anamalai Tiger Reserve, Southern Western Ghats.<br>c) Habit: : Mid elevation Wet evergreen Forest<br>d) Site History: : </p> <p>i) MANAMBOLI- The Mature Forest plot (10.357748° N, 76.889747° E; 825 m asl) is situated within a relatively undisturbed 200-hectare mid-elevation tropical wet evergreen rainforest tract at the core of the Anamalai Tiger Reserve (ATR). This area has been protected from logging and other significant disturbances since its establishment as a protected area in 1979.</p> <p>ii) CANDURA- The Secondary Forest plot (10.30855411° N, 76.83391853° E; 875 m asl) is situated within a 124-hectare rainforest remnant on the Valparai Plateau: the Candura rainforest remnant. The Candura site experienced episodic selective logging in the 1990s and early 2000s, with the last logging episode occurring in 2004. In the early 2000s, the understorey of the remnant was cleared for Vanilla (Vanilla planifolia) cultivation in the central and southern parts (abandoned in 2007), robusta coffee (Coffea canephora) in the northwestern corner (abandoned in the early 2000s), and pepper in 21 hectares in the northeastern part (established in 2015, abandoned in 2021).</p> <p>Climate: Humid tropical with about 2400 mm rainfall annually, falling mainly during the southwest monsoon.</p> <p><br><strong>4) TEMPORAL COVERAGE</strong></p> <p>a) Begins: 2017-11-30 (Year, Month, Day)<br>b) Ends: 2022-11-12 (Year, Month, Day)</p> <p><br>5) SAMPLING DESIGN AND METHODS </p> <p>a) Plot Design: Each 1 ha plot of 100 m × 100 m, sub-divided into 100 continuous sub-plots of 10 m × 10 m, was surveyed and mapped to maximum accuracy using a theodolite in the field, with grid corners permanently staked. <br>b) Data collection period and frequency: After the plot establishment in NOvember -- December 2017, the plots were recensused each year (around November). <br>c) Research Methods: All woody plant individuals with girth at breast height (GBH, at 1.3 m) ≥10 cm were tagged with numbered aluminum tags and spatially mapped. Plant species were identified using standard floral keys. Stem GBH was measured for all single stemmed individuals. For trees with buttresses, the GBH point of measurement (POM) was taken at 50 cm above the buttresses or at the height where the stem is regular. New saplings that recruited into the ≥10 cm GBH class were identified, mapped, tagged, and added to the monitoring. Stems that appeared to be dead were recorded at each monitoring and those that showed no signs of recovery in subsequent visits were recorded as mortality.</p> <p><br><strong>6) FILES INCLUDED</strong></p> <p>The dataset includes the following 9 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)</p> <p>01_README.txt<br>Metadata (this file) including information on the dataset explaining associated files and their contents.</p> <p>02_Candura_annual_census.csv <br>This contains the Annual census data with the following column headings: <br>site: Site name (Can = Candura)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>03_Manamboly_annual_census.csv<br>This contains Annual census data with the following column headings: <br>site: Site name (Man = Manamboli)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>04_Candura_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Can = Candura)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>05_Manamboli_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Man = Manamboli)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>06_Candura_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Can = Candura)<br>ymd: Date in YYYY/MM/DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>07_Manamboli_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Man = Manamboli)<br>ymd: Date in YYYY-MM-DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>08_Species_name_match.csv<br>This file provides the combined list of species codes updated taxonomy and successional guild. Scientific names were updated to current taxonomy using the species name matching tool of the Global Biodiversity Information Facility, GBIF (www.gbif.org).<br>sps: Species codes<br>query : Scientific name of the plant at the time of data collection: <br>scientificName: : with auther citation: <br>key: GBIF key<br>rank: Taxonomic rank or level of identification (GENUS, SPECIES)<br>kingdom: Taxonomic Kingdom (plants) provided by GBIF name matching tool: <br>phylum: Taxonomic Phylum provided by GBIF name matching tool<br>class: Taxonomic Class provided by GBIF name matching tool<br>order: Taxonomic Order provided by GBIF name matching tool<br>family: Taxonomic Family provided by GBIF name matching tool<br>genus: Taxonomic Genus provided by GBIF name matching tool<br>botanical_name: Updated scientific name of the species provided by GBIF name matching tool<br>habt_new: Successional guild of the species (Mature = mature forest species; Secondary = secondary successional species; Int - Introduced species)</p> <p>09_R_scrpt_for_manuscript.R<br>Text file with code in the R statistical and programming environment (www.r-project.org).</p> <p><br><strong>Reference</strong><br>Marthews TR, Riutta T, Oliveras Menor I, Urrutia R, Moore S, Metcalfe D, Malhi Y, Phillips O, Huaraca Huasco W, Ruiz Jaén M, Girardin C, Butt N, Cain R and colleagues from the RAINFOR and GEM networks (2014). Measuring Tropical Forest Carbon Allocation and Cycling: A RAINFOR-GEM Field Manual for Intensive Census Plots (v3.0). Manual, Global Ecosystems Monitoring network, http: //gem.tropicalforests.ox.ac.uk/.</p> <p> </p>
Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics
<p>Full-resolution representative data sufficient to repeat analyses for the work of: AE Wolf, MA Heinrich, IB Breinyn, TJ Zajdel, and DJ Cohen in "Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics".</p> <p>Please see the _README2.0.0.txt file for explanations on contents in this Zenodo repository.</p> <p>Relevant codes used in our analyses are available on Github (github.com/CohenLabPrinceton/ElectrotaxisSupracellularMemory).</p>
Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA
This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.
El Verde long-term invertebrate data - Luquillo Forest Dynamics Plot (LFDP)
The data set consists of 44 files: (1) abundance data for the walking stick, Lamponius portoricensis, (2) estimates of abundance (Minimum Number Known Alive) for 17 species of terrestrial snails, and (3) 42 files containing data on size and mark-recapture estimates of population size for the snails Caracolus caracolla and Nenia tridens from the Wet Season of 1995 to the Wet Season of 2019 (no dry season data for 1995 or after 2012). Note: MNKA estimates are not always identical to the number of individuals indicated in the Mark-Recapture data, because some individuals (e.g., those that were lost before being marked) had to be excluded from calculations for Mark-Recapture estimates of abundance. Note, no data were collected in 2020 or 2021 due to the COVID-19 pandemic. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: Coral Reef: Long-Term Coral Population and Community Dynamics: Annual Island Wide Coral Demography Survey 2011 ongoing
Demographic performance (recruitment, growth, and survival) are quantified annually for multiple individual colonies of the three most common genera (Acropora, Pocillopora, Porites) at both backreef and forereef sites. Each coral was tagged in 2011 and subsequently sampled again in 2012 to track colony growth and mortality dynamics. However, since 2013, investigators have transitioned to identifying coral through detailed mapping methodology and will continue to identify corals using this method in the subsequent years. The mapping system developed in 2013 provides data appropriate for detailed demographic study of coral on varying spatial scales around the island. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Long-term dynamics of soil organic matter and aboveground net primary production in a Chihuahuan Desert Grassland at the Sevilleta National Wildlife Refuge, New Mexico (1989-2014)
Drylands contain a third of the organic carbon stored in global soils; however, the long-term dynamics of soil organic carbon and soil organic matter (SOM) in drylands remain poorly understood relative to dynamics of the vegetation carbon pool. We examined long-term patterns in SOM against both climate and prescribed fire in a Chihuahuan Desert grassland in central New Mexico, USA. SOM was measured each spring and fall for 25 years (1989–2014) in unburned desert grassland and from 2003 to 2014 following a prescribed fire. SOM concentration from 0-20 cm depth did not show a clear long-term trend but fluctuated seasonally at both burned and unburned sites, ranging from a minimum of 0.9% to a maximum of 3.3%. SOM concentration declined nonlinearly in wet seasons and peaked in dry seasons. These results not only contrast with the positive relationships between aboveground net primary production and precipitation for this region, but also with previous reports of greater SOM in wetter sites across drylands globally, suggesting that space is not a good substitute for time in predicting the dynamics of dryland SOM. We suggest that declines in SOM in wet periods are caused by increased soil respiration, runoff, leaching, and soil erosion. In addition to tracking natural variability in climate, SOM concentration also decreased by 14% following prescribed fire, a response that magnified over time and has persisted for nearly a decade due to the slow recovery of primary production. Our results document the surprisingly dynamic nature of soil organic matter and its high sensitivity to climate and fire in this dryland ecosystem.
Meiobenthos abundance. Long-term variability and dynamics of estuarine meiobenthic populations for North Inlet Estuary, South Carolina, from 1972 to 1992, North Inlet LTER (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-nin/6/1. The abstract below was extracted from the Level 0 data package and is included for context: The original purpose of this research was to determine if natural meiobenthic assemblages exhibited continuity over time and to monitor several physical variables to determine if these influenced long-term temporal patterns. The most recent study focused on variation and the relations of meiobenthos abundance with environmental factors over 11 years. Typically marine benthic community studies are limited temporally and the majority of previously published 'longterm' meiofauna results (all taxa) were based on about a year's duration.
Skogaryd data used for the paper: Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat Model.
<p>Dataset of abiotic and carbon exchange variables for Skogaryd drained afforested peatland. The dataset include measurements of soil temperature, ground water level, and carbon exhange as well as modelled carbon fluxes performed with the model ForSAFE-Peat </p>
Long-term simulation of snow cover and its potential impacts on seasonal frost dynamics in croplands across southern Canada
<p><em>In northern climes, accurate simulation of thermal and hydrological budgets for farmlands during overwintering conditions is crucial to both an accurate prediction of spring flooding and the successful management of nutrient losses. As snow cover influences soil freezing dynamics, it has been hypothesized that reduced snow cover due to warmer winters might increase the depth and duration of frozen soil conditions. Nonetheless, such impacts remain poorly understood and, given the difficulty in measuring the depth of frozen soil, no long-term field experiment has documented these potential effects. The present study was designed to test this hypothesis. Drawing upon observed snow depth and soil temperature data collected from six research farms across Southern Canada over various time spans from 1989 to 2020, the Root Zone Water Quality Model, integrated with the Simultaneous Heat and Water model, was calibrated and validated. The potential influence of warmer winter on shifts in soil frost dynamics was evaluated by estimating the depth and duration of frozen soil for each farmland site under various RCP temperature scenarios using the RZ-SHAW model. Soil frozen depth in Eastern site increased with the increase of RCP temperature scenarios in some years, but decreased under the highest RCP temperature scenario. The monthly relationship between snow depth and soil frozen depth was determined through partial correlation analysis. Snow was most effective in alleviating soil freezing in the months of January and February, a period when snow cover depth was least affected by warming air temperatures. This paper suggests that Global warming induced-snow cover reduction would be site-specific and is </em>more likely to occur in <em>regions where energy lost through reduced snow cover would outweigh the energy gained through warmer air temperature.</em></p>
Long-term dynamics of trace elements concentrations in the organism of the shrews (Sorex) during the periods of high and reduction emissions from the copper smelter
<p>Data and code for mixed-model analysis for the article: </p> <p>Mukhacheva S.V. (2022) Long-term dynamics of trace elements concentrations in the organism of the shrews (Sorex) during the periods of high and reduction emissions from the copper smelter" // Russian Journal of Ecology. Vol. 5. </p> <p>Data provided by S.V. Mukhacheva</p> <p>Code provided by A.N. Sozontov</p>
The evolution, complexity and diversity of models of long-term forest dynamics
<p><span>1. To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.</span></p> <p><span>2. We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.</span></p> <p><span>3. Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.</span></p> <p><span>4. Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation.</span></p> <p><span>5. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity – with the exception of global models – can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.</span></p>
data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"
<p>data sets from "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"</p> <p>Monthly means ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" by Eliane Maillard Barras, Alexander Haefele, René Stübi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux.</p> <p><a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a></p>
Рис. 2. ΔоΛговременная Αинамика весенней чисΛенности трех виΑов уток (A — трескунка; B — касатки; C — шиΛохвости) на ΑебеΑинском стационаре Хинганского заповеΑника (показаны уровень значимости и 95-процентный ΑоверитеΛьный интерваΛ) Fig. 2. Long-term spring number dynamics of three duck species at the Lebedinsky Station of Khingansky State Nature Reserve with p-values and 0.95 confidence intervals. A — Gargany; B — Falcated Duck; C — Pintail in The results of long-term observation of waterfowl spring migration in Khingan Nature Reserve, Eastern Russia
Рис. 2. ΔоΛговременная Αинамика весенней чисΛенности трех виΑов уток (A — трескунка; B — касатки; C — шиΛохвости) на ΑебеΑинском стационаре Хинганского заповеΑника (показаны уровень значимости и 95-процентный ΑоверитеΛьный интерваΛ) Fig. 2. Long-term spring number dynamics of three duck species at the Lebedinsky Station of Khingansky State Nature Reserve with p-values and 0.95 confidence intervals. A — Gargany; B — Falcated Duck; C — Pintail
Data and results for manuscript "Imaging groundwater infiltration dynamics in karst vadose zone with long-term ERT monitoring"
<p>This data set contains raw and inverted data from an Electrical Resistivity Tomography (ERT) monitoring experiment conducted over a period of three years at the Rochefort Cave Laboratory (RCL) site in South Belgium. It highlights variable hydrodynamics in the karst vadose zone of Lorette Cave. More conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) are also included in the package, which aims at provide a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring/summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.