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32 results for “multidecadal”
Data and Code in support of Caterpillar abundance in a northern hardwood forest: exogenous effects, endogenous feedbacks, and multidecadal trends.
In this study, we analyzed caterpillar abundance and biomass measured over 50 years (1970 - 2021) in the Hubbard Brook Experimental Forest, New Hampshire, USA. We tested mechanisms for determination of caterpillar abundance that included weather, host plant quality, and predator abundance. This dataset includes data, R code, and spatial files supporting this study. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Biological and physical controls on multidecadal acidification in a eutrophic estuary
<p>Estuaries support many ecologically and economically important resources that are especially vulnerable to ocean acidification from rising anthropogenic CO<sub>2</sub>. However, complex local processes in estuaries complicate and may disguise long-term pH trends. For example, terrestrial nutrient runoff and marine coastal upwelling may exacerbate pH variability and declines. We investigated eutrophication impacts on acidification in a central California estuary, Elkhorn Slough, which receives high nutrient loads from intensive surrounding agriculture and upwelling of the California Current System. We examined drivers of acidification including nutrients, ecosystem metabolism, and upwelling by modelling pH trends over 20 years using a Generalized Additive Mixed Model at four sites from the National Estuarine Research Reserve Systemwide Monitoring Program and collected additional water samples to calculate aragonite saturation. Our models revealed acidification trends over two decades which were more pronounced near the marine inlet. Near the marine inlet, high nutrient levels and lower buffering are associated with the greatest rate of acidification in the estuary, which was four times greater than the trend from anthropogenic CO<sub>2</sub> alone. However, tidally restricted areas experienced a different acidification pattern. A tidally restricted site recorded higher mean pH and aragonite saturation and increased pH levels associated with stronger upwelling conditions supplying marine-sourced nutrients. Therefore, variable ecosystem metabolism and tidal cycles are threats to acidity in this location. The effects of enhanced seasonal cycles or long-term trends in different zones of the estuary have implications for monitoring with a temporal frequency and scale to capture coastal acidification risks in estuaries.</p>
Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984 -2022)
<p>The <strong>Portuguese burn severity atlas (1984 -2022)</strong> provides satellite-derived burn severity estimates of each historical fire with its start and end dates recorded and equal to or larger than<strong>100 ha</strong> for fires from 1984 to 2022 in Portugal. The fire perimeters are provided by the Instituto da Conservação da Natureza e das Florestas (ICNF) (https://sig.icnf.pt/portal/home/item.html?id=983c4e6c4d5b4666b258a3ad5f3ea5af). <strong>Landsat </strong>imagery is applied for the creation of this atlas. The burn severity indices applied within this atlas are differenced Normalized Burn Ratio (<strong>dNBR</strong>), Relative differenced Normalized Burn Ratio (<strong>RdNBR</strong>), Relative Burn Ratio (<strong>RBR</strong>), and index combining dNBR with enhanced vegetation index (<strong>dNBR-EVI</strong>).</p> <p>Since 1984 to 2022, the total burned area recorded in Portugal is 4.85 million ha. Only <strong>valid fires</strong>, which are fires equal to or larger than 100 ha with known dates, were considered for the burn severity estimates. The total area of valid fires is 3.29 million ha. The <strong>Portuguese Burn Severity Atlas</strong> provides estimates for 3.17 million ha, accounting for 65% of all fires and <strong>96% of valid fires</strong>.</p> <p>The <strong>Portuguese burn severity atlas </strong>is organized in subfolders, each entitled as the corresponding year and containing shapefile, maps and a table with details on pairs of images used for burn severity estimates. Within each subfolder, the following data are stored:</p> <ul> <li> the annual fires’ perimeters shapefile (.dbf, .prj, .shp, .shx)</li> <li>dNBR map (.tiff)</li> <li>RdNBR map (.tiff)</li> <li>RBR map (.tiff)</li> <li>dNBR-EVI map (.tiff)</li> <li>confidence map: average “SUITABILITY in each pixel within the area of the fire (.tiff)</li> <li>comma separated value (.csv) file containing details on the pair of images used for burn severity estimates (year, ID, iteration number, pre-fire time lag(day), pre-fire cloud%, pre-fire suitability (%), post-fire time lag (day), post-fire cloud%, post-fire suitability(%), confidence in the iteration(%), area with dNBR estimation(ha), area of fire (ha), fire i number (to refer in GEE code), dNBR offset value, RdNBR offset value, RBR offset value, and dNBR-EVI offset value).</li> </ul> <p>To the best of our knowledge, <strong>Portuguese Burn Severity Atlas </strong>is the first open access atlas providing burn severity estimates of historical fires for an entire European country, in this case, <strong>Portugal</strong>, with 38 years of coverage. Target audience who can benefit from this atlas can be policymakers at national levels, field managers, project managers, and agency and academic researchers.</p> <p><strong>***</strong> The second version of this atlas provides updated fire data and burn severity estimates of fires from 1984 to 2000 with corrected dates area equal or larger than 100 ha. Moreover, for 2012, aside from burn severity estimates provided via imagery from Terra abroad Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat7-derived maps are included. </p>
On the role of the ocean in the Atlantic multidecadal oscillation
<p>Atlantic multidecadal Oscillation (AMO) index and its surface-forced and ocean dynamics-forced components in a fully-coupled and partially-coupled experiments</p>
FIGURE 3 in Multidecadal fishers' knowledge reveals overexploitation of sharks in southeastern Brazil
FIGURE 3 | Best day's catches (number of individuals caught) according to hook and line fishers of Arraial do Cabo. A. Spinner shark, Carcharhinus brevipinna, with fourth order polynomial regression (r² = 0.02, p = 0.8); B. Shortfin mako shark, Isurus oxyrinchus, with second order polynomial regression (r² = 0.14, p = 0.46); C. Sandbar shark, Carcharhinus plumbeus, with third order polynomial regression (r² = 0.08, p = 0.02).
FIGURE 5 in Multidecadal fishers' knowledge reveals overexploitation of sharks in southeastern Brazil
FIGURE 5 | Photographs showing past and present of beach seine catches of spinner shark, Carcharhinus brevipinna, caught in Arraial do Cabo, Brazil. A. Individuals caught in 1979; B. In 1996; C. In 2005; and D. In 2014. Images were kindly provided by interviewed fishers of Arraial do Cabo.
FIGURE 4 in Multidecadal fishers' knowledge reveals overexploitation of sharks in southeastern Brazil
FIGURE 4 | Spatial distribution of fishing grounds of best day's shark catches in Arraial do Cabo (number of individuals caught). A. Spinner shark, Carcharhinus brevipinna, catches from 1979 to 2005 and B. 2006 to 2019; C. Shortfin mako shark, Isurus oxyrinchus, catches from 1962 to 2005 and D. 2006 to 2019; E. Sandbar shark, Carcharhinus plumbeus, catches from 1968 to 2005 and F. 2008 to 2019.
FIGURE 2 in Multidecadal fishers' knowledge reveals overexploitation of sharks in southeastern Brazil
FIGURE 2 | Spinner shark, Carcharhinus brevipinna, catches according to beach seine fishers in Arraial do Cabo (with second order polynomial regression line shown r2 = 0.09, p = 0.5).
Biological and physical controls on multidecadal acidification in a eutrophic estuary
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Multidecadal Time Series of Measured Chlorophyll-a in Lakes and Estuarine-Coastal Ecosystems, 1966-2024
The photosynthetic pigment chlorophyll-a is a commonly measured index of phytoplankton biomass and water quality across all aquatic ecosystem types. Some monitoring and research programs have sustained chlorophyll-a measurements for decades at monthly or higher frequency. Each of these time series is an invaluable record of phytoplankton variability at a particular location. The patterns of that variability have been essential for identifying the underlying processes of phytoplankton change at time scales of days, months, seasons, years and decades. Multidecadal series are rare and valuable because they provide empirical records of phytoplankton changes over the recent decades of unprecedented global change. These records also provide an empirical basis for comparing patterns and rates of change across geographic regions and ecosystem types. This data package contains multidecadal time series of measured chlorophyll-a concentration in three ecosystem types: 134 freshwater lakes (including a small number of reservoirs) that do not freeze; 78 high latitude lakes that do freeze; and 176 coastal ecosystems defined as water bodies where freshwater and seawater mix, including estuaries, coastal bays and lagoons, tidal rivers, and the Baltic Sea. Although there are other published compilations of chlorophyll-a time series, this package was compiled specifically to report observations made at monthly or higher frequency and sustained over multiple decades. The mean time series duration in this package is 33 years, and the mean number of sampling dates per site was 503 (range 186 to 2381). Thus, this data package provides an empirical basis for analyses to measure and compare decadal-scale patterns and rates of phytoplankton biomass variability between inland lakes and water bodies at the land-ocean interface. All chl-a measurements reported here were accessed from published repositories, except these four sites. We acknowledge and thank the following data providers for perm
Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
<p><span>Temperature has a large influence on insect abundances, thus under climate change, identifying major drivers affecting pest insect populations is critical to world food security and agricultural ecosystem health. Here, we conducted a meta-analysis with data obtained from 120 studies across China and Europe from 1970 to 2017 to reveal how climate and agricultural practices affect populations of wheat aphids. H</span><span>ere</span><span> we showed that aphid loads on wheat had distinct patterns between these two regions, with a significant increase in China but a decrease in Europe over this time period. Although temperature increased over this period in both regions, we found no evidence showing climate warming affected aphid loads. Rather, differences in pesticide use, fertilization, land use, and natural enemies between China and Europe may be key factors accounting for differences in aphid pest populations. These long-term data suggest that agricultural practices impact wheat aphid loads more than climate warming. </span></p>
Multidecadal Variation in the Seasonal Predictability of Winter PNA and Its Sources
<p>This directory contain the climate indices from CESM ensemble hindcasts during period 1900-2014 that are used for a manuscript ("Multidecadal Variation in the Seasonal Predictability of Winter PNA and Its Sources") submitted for Geophysical Research Letters.</p> <p>Description: </p> <p>The PNA index is defined by using the pointwise method , i.e., a linear combination of the normalized 500 hPa geopotential height anomalies (Z) at the four active centers: PNA = 1/4[Z(20°N, 160°W) – Z (45°N, 165°W) + Z (55°N, 115°W) – Z (30°N, 85°W)].The predicted seasonal mean PNA index is calculated based on the CESM monthly mean 500 hPa geopotential height prediction in December, January and February (DJF), i.e., with a 2-month lead time. The ensemble mean PNA index is the mean of 20 ensemble seasonal mean PNA indices. </p> <p>The predicted seasonal mean Niño 3.4 index is calculated as the area average of SSTA in the domain of 170°W-120°W, 5°S-5°N based on the CESM monthly mean SST in December, January and February (DJF), i.e., with a 2-month lead time. The linear signal of global warming is filtered out from the SSTA. The ensemble mean Niño index is the mean of 20 ensemble seasonal mean Niño 3.4 indices. </p> <p>The predicted seasonal mean PDO index is calculated as the time series associated with the first leading empirical orthogonal function (EOF) pattern of the winter mean (DJF) North Pacific SSTA (north of 20°N) based on the CESM monthly mean SST in December, January and February (DJF), i.e., with a 2-month lead time. The linear signal of global warming is filtered out from the SSTA. The ensemble mean PDO index is the mean of 20 ensemble seasonal mean PDO indices.<br> </p>
FIGURE 1 in Multidecadal fishers' knowledge reveals overexploitation of sharks in southeastern Brazil
FIGURE 1 | Map of Arraial do Cabo region, Rio de Janeiro, Brazil, and study sites.
Armitage et al., 2024. Multidecadal changes in coastal benthic species (primary and secondary data: SOTEAG)
<p>The primary data forms part of an ongoing monitoring programme by the Shetland Oil Terminal Environmental Advisory Group (SOTEAG). SOTEAG has undertaken biennial benthic surveys in Sullom Voe and the surrounding areas since 1974 to determine the status of infaunal communities and seafloor sediments. For more information on SOTEAG and the types of monitoring they practice please see: <a href="https://soteag.org.uk/">https://soteag.org.uk/</a>. A full description of sampling methods can be found here: https://soteag.org.uk/environmental-monitoring/monitoring-reports/</p> <p>These published data are a subset of the original SOTEAG data. They were used in a 32-year analysis of benthic infaunal invertebrates to investigate changes in species and trait composition, as well as detect long-term temperature-related shifts in benthic species and communities. The manuscript is entitled “Multidecadal changes in coastal benthic species composition and ecosystem functioning occur independently of temperature-driven community shifts” by Armitage et al., 2024. It should be noted that many of these data are a subset of the original and may have been transformed or standardised for statistical analyses. Below is a description of each .csv file, and whether the data are primary or secondary data, and how they are used in the analyses for the published paper. For details on how the data were reduced, please see the publication. In summary, a total of 15 stations were selected with a biennial year range of 1986 – 2018. The depth range covered 4 – 58m, where stations were classified into shallow, intermediate, and deep depth groups. </p> <p><strong>Env.csv</strong></p> <p><em>Primary data</em>: The environmental data that were collected during sampling by SOTEAG. The data are in long format with each column denoting: Station, Year, Water depth (m), Gravel %, Sand %, Mud %, Grain size, Water temperature (ᵒC), and water salinity. </p> <p><strong>Indices.csv</strong></p> <p><em>Secondary data</em>: These data are the various species and functional indices created from an analysis using species and trait data. The data are in long format and have row names consisting of the station and year, as well as column headings species richness (sp.rich), total species abundance (abundance) species diversity: Shannon (shannon), species diversity: Simpson (simpson), species evenness (evenness), functional richness (FRic), functional evenneness (FEve), functional diversity, also knowns as dispersion (FDis), functional redundancy (Red) species vulnerability (Vul), sampled station (Station), year of sampling (Year), classified depth group (Depth_group), and the decade (Decade). </p> <p><strong>Species.csv</strong></p> <p><em>Primary data</em>: This is the SOTEAG species abundance/community list that has been reduced (rare species were removed, a subset of stations were selected, and data were averaged and standardised across replicates). </p> <p><strong>Species_classification_all.csv </strong></p> <p><em>Secondary data</em>: This is extra supporting information for the species list highlighting the taxonomic ranking (coloumns) and species (rows). </p> <p><strong>STI_and_EcoFun_per_sp.csv</strong></p> <p><em>Secondary data</em>: This is data produced from a combination of species abundance and traits. The first two columns are row ID and species names. Columns 3-12 describe five ecosystem functions with the calculations/methodology provided in the publication. These data show the mean and scaled (0-1) scores for each function. The column headings equate to bioturbation (Bio), sediment stability (Sed), nutrient recycling (NutR), low trophic position (lowTP), high trophic position (highTP). The following columns X0 – X100 represent the quantiles of temperature affinities, with 50 being the 50% mark. The last column is the number of records (nrec) related to the thermal affinity search. </p> <p><strong>STImacroSoteag.csv </strong></p> <p><em>Secondary data</em>: Species thermal affinities (methods described in publication) with the first two columns as row ID and species names. The following columns X0 – X100 represent the quantiles of temperature affinities, with 50 being the 50% mark. The last column is the number of records (nrec) related to the thermal affinity search. </p> <p><strong>Traits.csv</strong></p> <p><em>Primary data</em>: Trait data where each species is listed as a row and traits as a coloumn heading. For trait column names please see publication. Note that the ranges of scores given to a species affinity to a trait is 0-3, which may be split depening on the affinity of the species (i.e., fuzzy coding). </p> <p><strong>Traits_EcoF.csv</strong></p> <p><em>Secondary data</em>: This is data produced from a combination of species abundance and traits. The first two columns are row ID and species names. Columns represent the trait used for the calculation of the Ecosystem Function (please see equations 2 – 6 in the publication). The column headings equate to bioturbation (Bio), sediment stability (Sed), nutrient recycling (NutR), low trophic position (lowTP), high trophic position (highTP), while the trait abbreviations can be found in Table 1 of the publication. The scores follow the fuzzy coding approach highlighted above (Traits.csv). </p> <p><strong>TrendsVsSTIHats.csv</strong></p> <p><em>Secondary data</em>: Results from species thermal index analysis and used to plot figure 4 of the main publication. Data is in long format. </p> <p><strong>tsSSTdata.csv</strong></p> <p><em>Primary data:</em> Sea surface temperatures used in the species distribution model to fit species thermal indices. See publication for details (section 2.3). </p> <p><strong>Vulnerability.csv </strong></p> <p><em>Secondary data:</em> A species index that was a result from the main species abundance/community data. Index values are given for each species (rows) by station/year (columns). Community means of the index can be found in “indices.csv”.</p>
Multidecadal-centennial record of Holocene climate changes
<p>Based on the 8 meticulously detailed and accurately dated records detailing climate changes in the Northern Hemisphere over the Holocene, obtained by different proxies from different climatic zones we constructed stack of climate changes. The selected records encompass: δ18O data from the NGRIP Greenland ice core(Rasmussen et al., 2014), Greenland Summit temperature reconstruction using nitrogen and argon isotopes of trapped air within GISP2 ice core bubbles(Döring and Leuenberger, 2022; Kobashi et al., 2017), ice rafted debris (IRD) accumulation data from sediment cores VM29-191(Bond et al., 2001) and MD99-2269(Moros et al., 2006) in the North Atlantic, with adjustments to the age model following Stoner et al.(Stoner et al., 2007), Ti content in sediments from ODP Site 1002 in the northern Cariaco Basin(Haug et al., 2001), records of East Asian summer monsoon (EASM) changes from δ18O stalagmites in Chinese Dongge(Wang et al., 2005) and Sanbao(Dong et al., 2010) Caves, and composite climatic record from Icelandic lakes (Geirsdóttir et al., 2013). All records were linearly interpolated to 10-yr resolution and then detrended and normalized by subtracting the mean value and dividing by the standard deviation. The resulting stack was smoothed using a 150-yr moving average window.<br>Cooling rate was calculated as negative rate of the climate stack changes with 30-yrs resolution.</p>
Data for "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multidecadal trend in the Warm Arctic Cold Eurasia pattern"
<p>Here the model experiment data used to create the figures in the article "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the Warm Arctic Cold Eurasia pattern" are provided.</p>
Data from: Landsat-based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations
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Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
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Adult survival in a small seabird, Hydrobates leucorhous, covaries with the Atlantic Multidecadal Oscillation over the past six decades
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Data for "Millennial modulation of Atlantic Multidecadal Variability in a climate model"
<p>Processed data and Jupyter notebooks for manuscript "Millennial modulation of Atlantic Multidecadal Variability in a climate model" by Joakim Kjellsson and Wonsun Park</p>
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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.