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180 results for “Environmental Drivers”
Observed phenological indicators and environmental drivers at global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains plant phenological data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data here are derived from raw "phenocam" camera data collected at two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and basic color and greenness data extracted from those images are available in a companion dataset on EDI (knb-lter-jrn.210574001). This dataset includes the derived annual and quarterly phenological indices and greenness indices for each plot monitored by phenocams, and temperature and precipitation variables aggregated to the same frequency. The dataset also includes R code and input files used to generate these derived data. See Currier and Sala 2022 for more details. This study is ongoing.
Dataset: Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean
<p>This dataset is linked to this manuscript entitled "Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean" published in Elementa: Science of the Anthropocene (<a href="http://doi.org/10.1525/elementa.430">http://doi.org/10.1525/elementa.430</a>). Please find the abstract below:</p> <p>The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice improves the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and species composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (> 0.1–0.2 mol photons m<sup>–2</sup> d<sup>–1</sup>), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll <em>a </em>concentration) was predicted best by winter water Si(OH)<sub>4</sub> and PO<sub>4</sub><sup>3–</sup> concentrations, as well as Si(OH)<sub>4</sub>:NO<sub>3</sub><sup>–</sup> and PO<sub>4</sub><sup>3–</sup>:NO<sub>3</sub><sup>–</sup><sub> </sub>drawdown ratios, but not NO<sub>3</sub><sup>–</sup> concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or <em>Phaeocystis</em>), driven primarily by the winter nitrate:silicate (NO<sub>3</sub><sup>–</sup>:Si(OH)<sub>4</sub>) ratio and the under-ice light climate. <em>Phaeocystis</em> co-dominated in low Si(OH)<sub>4</sub> (i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub> molar ratios > 1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)<sub>4</sub> was high (i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub> molar ratios < 1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.</p>
Data from "Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient"
Synthesized fish, benthic invertebrate, and water quality dataset used for analysis in: Rogers, T., S. Bashevkin, C. Burdi, D. Colombano, P. Dudley, B. Mahardja, L. Mitchell, S. Perry, and P. Saffarinia. 2022. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. preprint, EcoEvoRxiv. https://doi.org/10.32942/X2MK5Z
Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
<p>Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea</p>
Dataset: Greenland environmental drivers and biota responses
<p>This dataset includes data on sea surface temperatures, sea ice concentration, sea ice seasonality, salinity, runoff form the Greenland ice sheet, cholorophyll a, and a litterature review. The data is divided into six regions around Greenland stretching 200 km of the coastline. Each region spans 9 degrees latitude.</p>
Data for: Luo et al., Expiratory aerosol pH: the overlooked driver of airborne virus inactivation, Environmental Science and Technology, 10.1021/acs.est.2c05777
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figures 1 and S3 (inactivation curves for IAV, SARS-CoV-2 and HCoV-229E)</p> <p>Figure 1 (rate constants)</p> <p>Figure 2 (EDB analysis of SLF)</p> <p>Figure S1A (zetasizer analysis to measure virus aggregation)</p> <p>Figure S1B (renilla and plaque assay data for viruses exposed to pH 5, 6 and 7)</p> <p>Figure S4A (EDB analysis of different SLF samples; raw data)</p> <p>Figure S4Amean (EDB analysis of different SLF samples; mean values)</p> <p>Figure S5 (EDB analysis of nasal mucus)</p> <p>Figure S8 (EDB analysis of slow crystal growth stage of SLF and nasal mucus)</p> <p>Figure S13 and S14 (literature data on inactivation of IAV and SARS-CoV-2 in aerosol particles)</p> <p> </p> <p> </p>
Age-0 fish abundances and putative environmental drivers for the Sacramento-San Joaquin Delta, California, 1980 to 2020.
Data on fall (September-December) age-0 abundances of a suite of relatively abundant/common fishes and a suite of likely environmental driver variables. All data are derived from publicly available sources as cited below, except for determination of age-0 maximum length thresholds, which were derived from examination of length-frequency histograms for fall months. Fish data are from https://doi.org/10.6073/pasta/0cdf7e5e954be1798ab9bf4f23816e83. Water temperature, chlorophyll-a, and Secchi depth data are from https://doi.org/10.6073/pasta/42b3d889ffa056030f953aed85b5621e. Zooplankton abundance data are from (https://doi.org/10.6073/pasta/89dbadd9d9dbdfc804b160c81633db0d). Sea surface temperature data are from (https://doi.org/10.6075/J0S75GHD). Ocean upwelling data are from (https://oceanview.pfeg.noaa.gov/products/upwelling/dnld). Flows data are from the DAYFLOW model (https://data.cnra.ca.gov/dataset/dayflow). Environmental driver data are annualized by water year and, where appropriate, aggregated to a single, putatively spatially representative time series.
Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
<p>Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. In this study, we compare the relative efficacy of linear and second-order LTRE analyses in capturing changes in population growth rate caused by environmental driver changes. To explore this question, we analyze demographic data collected for three long-lived plant species: <em>Ardisia escallonioides</em> (Pascarella & Horvitz, 1998), <em>Silene acaulis</em>, and <em>Bistorta vivipara</em> (Doak & Morris, 2010). This repository includes data files containing vital rate (survival, growth, reproduction) observations or models for our three case studies, as well as an R script in which we use these demographic data to calculate linear and second-order LTRE approximations of changes in population growth rate for each system and generate the figures we present in our paper.</p>
Data from: Phenology of penaeid shrimp nursery habitat use: trends and environmental drivers over four decades
<p>These datasets are those used in analysis published in Batchelder et al. 2024 (for full abstract see: https://doi.org/10.3354/meps14741). </p>
Data from: Environmental drivers of population-level variation in the migratory and diving ontogeny of an Arctic top predator
<p>The development of migratory strategies that enable juveniles to survive to recruitment is critical for species that exploit seasonal niches. For animals that forage via breath-hold diving this requires a combination of both physiological and foraging skill development. Here, we assess how migratory and dive behaviour develop over the first months of life for a migratory Arctic top predator, the harp seal, tracked using animal-borne satellite relay data loggers. We reveal similarities in migratory movements and differences in diving behaviour between juveniles from breeding populations in the Northwest Atlantic and Greenland Sea. In both regions, periods of resident and transient behaviour during migration were associated with proxies for food availability; sea ice concentration and water depth. However, while ontogenetic development of dive behaviour was similar for both groups of juveniles over the first 25 days, after this time Greenland Sea animals performed shorter and shallower dives and were more closely associated with sea ice than Northwest Atlantic animals. Together, these results highlight the role of both intrinsic and extrinsic factors in shaping early-life behaviour. Differences in the environmental conditions experienced during early-life may shape how populations respond to the rapid changes occurring in the Arctic ocean ecosystem.</p>
Environmental drivers of biseasonal anthrax outbreak dynamics in two multi-host savanna systems
<p>Environmental factors are common forces driving infectious disease dynamics. We compared inter-annual and seasonal patterns of anthrax infections in two multi-host systems in southern Africa: Etosha National Park, Namibia, and Kruger National Park, South Africa. Using several decades of mortality data from each system, we assessed possible transmission mechanisms behind anthrax dynamics, examining 1) within- and between-species case correlations, and 2) associations between anthrax mortalities and environmental factors, specifically rainfall and the Normalized Difference Vegetation Index (NDVI). Anthrax cases in Kruger had wide inter-annual variation in case numbers, and large outbreaks seemed to follow roughly a decadal cycle. In contrast, outbreaks in Etosha were smaller in magnitude and occurred annually. In Etosha, the host species commonly affected remained consistent over several decades, although plains zebra (<em>Equus quagga</em>) became relatively more dominant. In Kruger, turnover of the main host species occurred after the 1990s, where the previously dominant host species, greater kudu (<em>Tragelaphus strepsiceros</em>), was replaced by impala (<em>Aepyceros melampus</em>). In both parks, anthrax infections showed two seasonal peaks, with each species having only one peak in a year. Zebra, springbok (<em>Antidorcas marsupialis</em>), wildebeest (<em>Connochaetes taurinus</em>) and impala cases peaked in wet seasons, while elephant (<em>Loxodonta africana</em>), kudu and buffalo (<em>Syncerus caffer</em>) cases peaked in dry seasons. For common host species shared between the two parks, anthrax mortalities peaked in the same season in both systems. Among host species with cases peaking in the same season, anthrax mortalities were mostly synchronized, which may imply similar transmission mechanisms or shared sources of exposure. Between seasons, outbreaks in one species may contribute to more cases in another species in the following season. Higher vegetation greenness was associated with more zebra and springbok anthrax mortalities in Etosha, but fewer elephant cases in Kruger. These results suggest that host behavioral responses to changing environmental conditions may affect anthrax transmission risk, with differences in transmission mechanisms leading to multi-host biseasonal outbreaks. This study reveals the dynamics and potential environmental drivers of anthrax in two savanna systems, providing a better understanding of factors driving biseasonal dynamics and outbreak variation among locations.</p>
Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]
<p>Supplementary Material of the PhD [Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]</p> <p> </p>
Fig. 9 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 9: Histogram of the Mantel test assessing the relationship between genetic and morphologic distance for Gobius niger. Sim: simulations; Frequency: frequency values of the correlation between the genetic and morphologic distances. The dot represents the original value of the correlation between the distance matrices.
Fig. 6 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 6: PCA of the morphological variables of Gobius niger (standard length, SL; body height, BH; head length, HL; snout length, SnL; eye diameter, ED; first dorsal fin, DF1; second dorsal fin, DF2; anal fin, AF; pectoral fin, PF; ventral fin, VF) with projection of phenotypic groups. PC1 vs. PC2 and PC2 vs. PC3. The percentage of variation explained by each PC axis is given within parentheses.
Fig. 3 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 3: Cluster analysis associated with the similarity profile test (SIMPROF), based on abundances of Gobius niger, reveals reciprocal relations among the 20 sampled stations in the Marchica Lagoon using the Bray–Curtis distance.
Fig. 2 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 2: Picture of Gobius niger from the Marchica Lagoon showing the main measurements taken: total length (TL), standard length (SL), head length (LT), snout length (SnL), body height (BH), and eye diameter (ED).
Fig. 7 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 7: Linear regression of the principal component score axis (PC1) from morphometric measurements on the log standard length of Gobius niger with projection of phenotypic groups.
Fig. 8 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 8: Haplotype network constructed from 16S rDNA sequences of Gobius niger. The size of a particular circle reflects the haplotype frequency. The numbers indicate the nodes.
Fig. 1 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 1: Map showing the geographical localization of the Marchica Lagoon and the sampling stations of Gobius niger.
Fig. 4 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 4: Two-dimensional redundancy analysis (RDA) ordination representing the spatial distribution of Gobius niger related to the predictor variables selected through the best linear models based on distance (DISTLM). SM: suspended matter.
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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.