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1,662 results for “Phenology”
Phenological metrics for Protected Area "NorthernLimestone", MODIS terra tile h18v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "DanubeDelta", MODIS terra tile h20v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "Hardangervidda-h18v03", MODIS aqua tile h18v03
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "WaddenSea", MODIS aqua tile h18v03
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "PenedaGeres", MODIS terra tile h17v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "MurgiaAltaPark", MODIS aqua tile h19v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Data for: "Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA)"
<p>This dataset contains the spring and autumn migration phenology dataset used in Haest <em>et al.</em> (2020) to determine the drivers of migration phenology of Brazilian free-tailed bats at Bracken Cave (USA) over the period 1995-2017. The phenology dataset was derived from nightly colony population sizes estimated using weather radar data (Stepanian <em>et al.</em>, 2018). See the Materials and Methods section in Haest <em>et al.</em> (2020) for more details on the dataset. </p> <p>References:</p> <p>Haest, B., Stepanian, P. M., Wainwright, C. E., Liechti, F., & Bauer, S. (2021). Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA). <em>Global Change Biology</em>, 27(4), 768-780. <a href="https://doi.org/10.1111/gcb.15433">https://doi.org/10.1111/gcb.15433</a></p> <p>Stepanian, P. M., & Wainwright, C. E. (2018). Ongoing changes in migration phenology and winter residency at Bracken Bat Cave. <em>Global Change Biology</em>, <em>24</em>(7), 3266–3275. <a href="https://doi.org/10.1111/gcb.14051">https://doi.org/10.1111/gcb.14051</a></p> <p> </p>
Phenology data set of plants and birds and other taxonomic groups, as well as agrarian activities and abiotic phenomena from Latvia, 1970-2018
<p>A data set of phenological observations of plants, birds, as well as agrarian activities and abiotic phenomena from Latvia, 1970-2018 is presented. The data include limited number of observations of insects, amphibians, mammals, mushrooms, mollusks and fishes as well. The data was collected by voluntary observers (citizen scientists) and published as paper based yearly bulletins. It includes almost 48 000 individual observations of 159 different phenological phases from 103 locations in Latvia. Each entry is comprised of following fields:</p> <ol> <li>Station: name of the observation station</li> <li>Year: year of observation</li> <li>Season: season of observation as indicated in the primary publication</li> <li>Species: English name of the species observed or description of phenomena observed in case of abiotic occurrences</li> <li>Species Latin: Latine name of the species observed</li> <li>Taxonomic_group: taxonomic group of the species observed or grouping of non-biological phases (“Abiotic” for meteorological phenomena and “Agrarian” for agrarian activities)</li> <li>Phenophase: description of phenological phase observed</li> <li>BBCH: attributed BBCH code for phenological phase observed, where applicable</li> <li>Date: date of the first observation of the phase</li> <li>DoY: day of the year of the first observation of the phase</li> <li>Implausible: flag indicating of the reported date of phenological phase is highly implausible (TRUE) or realistic (FALSE)</li> <li>Wrong_order: flag indicating if the order of the reported phases at a given station and year is not realistic (TRUE) or realistic (FALSE)</li> </ol>
Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology
<p>What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators.</p> <p>Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories.</p> <p>Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography.</p> <p><strong>The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting. </strong></p>
Dataset from: Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network
<p>Original data and code for the study:</p> <p>Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology.</p> <p>The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain.</p> <p>The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”.</p> <p>The code contains the R scripts and MATLAB scripts that we used for this study.</p> <p>For details please see the file “Readme.txt”, and the research paper.</p>
Using RS-DAT to study continental-scale phenology at high-spatial resolution
This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.
A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula
<p>This data repository is a permanent archive of the results presented in the associated publication (Turner et al. 2024, Marine Ecology Progress Series, <a href="https://doi.org/10.3354/meps14567">https://doi.org/10.3354/meps14567</a>). The objective of this study was to investigate phytoplankton phenology patterns west of the Antarctic Peninsula using satellite ocean color remote sensing data. This dataset extends from 80<sup>o</sup>W to 55<sup>o</sup>W longitude and from 70<sup>o</sup>S to 60<sup>o</sup>S latitude. The data span the time period September 1997 through August 2022. This dataset includes the data and code used to create the figures in the publication. The data in this repository include chlorophyll-a concentration (Chl-a), dates of phytoplankton bloom start date and phytoplankton bloom peak date, photosynthetically active radiation (PAR), sea surface temperature (SST), wind speed, and dates of sea ice retreat and advance. Downloaded spatially-subsetted data files are included as netCDF files (extension .nc) compressed into .zip archives. Additional files used to perform the analyses and make the figures are included as MATLAB scripts and MATLAB data files (extensions .m and .mat, respectively). </p> <p>Recommended citation:</p> <p>Turner, Jessica S., (2024) A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula. Zenodo. https://doi.org/10.5281/zenodo.10790613</p>
Ecological, flowering phenology, morphological and seed production of three sympatric dioecious Chamaedorea palms from Costa Rica
<p>The data in the file was used to estimate the factors shaping seed production in three sympatric dioecious Chamaedorea palms in Costa Rica during the 2011-2012 season. The file contains the following fields:</p> <ol> <li>Species. The name of the species: C. costaricana, C. macrospadix and C. tepejilote</li> <li>ID. Identifier for each studied individual female plant.</li> <li>infl. Identifier for each sampled inflorescence from each sampled female.</li> <li>census.date: flowering date of each inflorescence.</li> <li>days.since.oct14: number of days since the first Chamaedorea inflorescence flowered.</li> <li>days.since.1st.flr: number of days since the first Chamaedorea inflorescence of each species flowered.</li> <li>sync.costa: flowering overlap with C. costaricana males.</li> <li>sync.macro: flowering overlap with C. macrospadix males.</li> <li>sync.tepe: flowering overlap with C. tepejilote males.</li> <li>neartest.female: distance to the nearest synchronously flowering <span>conspecific </span>female.</li> <li>male.5m: number of synchronously flowering <span>conspecific </span>male individuals in a 5m radius</li> <li>male.10m: number of synchronously flowering <span>conspecific </span>male individuals in a 10m radius</li> <li>female.5m.edco: number of synchronously flowering <span>conspecific </span>female individuals in a 5m radius, after applying Ripley's (1977) edge correction.</li> <li>female.10m.edco: number of synchronously flowering <span>conspecific </span>female individuals in a 10m radius, after applying Ripley's (1977) edge correction.</li> <li>male.5m.edco: number of synchronously flowering <span>conspecific </span> male individuals in a 5m radius, after applying Ripley's (1977) edge correction.</li> <li>male.10m.edco: number of synchronously flowering <span>conspecific </span>male individuals in a 10m radius, after applying Ripley's (1977) edge correction.</li> <li>no.stems: specific for C. costaricana, number of stems per individual.</li> <li>height: height of the flowering stem in cm.</li> <li>leaves: number of leaves of the flowering stem</li> <li>leaflets: number of leaflets of the youngest leaf of the flowering stem</li> <li>leaf.rachis: length in cm of the youngest leaf of the flowering stem</li> <li>floral.rachis: length in cm of the inflorescence's rachis</li> <li>peduncle: length in cm of the inflorescence's peduncle</li> <li>no.spikes: number of spikes of the inflorescence</li> <li>no.flowers: number of flowers per inflorescence</li> <li>no.fruits: number of single-seeded fruits per inflorescence</li> </ol>
Warming drives phenological changes in coastal zooplankton
<p>This repository contains zooplankton observations and model data used in the article Forsblom, Stoffers, Lindén, Lehtiniemi and Engstöm-Öst (2024) Warming drives phenological changes in coastal zooplankton. Marine Biology (in press).</p>
Intra-urban variations in land surface phenology in a semi-arid environment
<p>Data repository for 'Intra-urban variations in land surface phenology in a semi-arid environment', ERL</p> <p>Contact: Ben Crawford, University of Colorado Denver (benjamin.crawford@ucdenver.edu)</p> <p>Data description:</p> <ul> <li>NDVI.zip: <ul> <li>MODIS NDVI geotif rasters for Denver study area</li> <li>Additional metadata provided in subdirectories</li> </ul> </li> <li>LST.zip: <ul> <li>Landsat LST geotif rasters for Denver study area</li> </ul> </li> <li>Tair.zip <ul> <li>Seasonal modeled air temperatures for Denver study area (as described in the manuscript and supplemental information)</li> </ul> </li> <li>Den470_LandCover_250m_WGS.tif <ul> <li>Denver study area 2018 land cover fractions, derived from 1 m data at https://data.drcog.org/</li> </ul> </li> </ul> <p> </p>
Data for "Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird"
<p><strong>Abstract</strong></p> <p>To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, <em>Montifringilla nivalis</em>, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as potential causes for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.</p>
Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments
<p>This dataset is related to the data paper published in Ecology: Filipiak et al, in press, <em>Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments</em>; doi: to be provided when available. This paper must be always properly cited when using the database. Please use any scientific full citation format. <br> <br> <strong>Abstract</strong><br> To predict the quantity and quality of the food available for pollinators in various landscapes over time, it is necessary to collect detailed data on pollen, nectar, and sugar production per unit area and the flowering phenology of plants. Similar data are needed to estimate the contribution of plants to the functioning of food webs via the flow of energy and nutrients through the soil-plant-nectar/pollen-consumer pathway. Current knowledge on this topic is fragmented. This is the first database to compile data on the various food resources produced by 1612 different plant species, belonging to 755 genera and 133 families, including crops and wild plants, annuals and perennials, animal- and wind-pollinated plants, and weeds and trees growing in different ecosystems under various environmental conditions. The dataset consists of 103 parameters related to the traits of plant species, as well as to geographical and environmental factors, allowing for precise calculations of nectar, pollen and energy provided by plants and available to consumers in the considered flora or ecosystem on a daily basis throughout the year. These parameters, gathered by us and extracted from the available literature, describe pollen, nectar and sugar production (where applicable, in mass, volume and concentration units), honey yield, the timing and duration of flowering, flower longevity, numbers of plants and flowers per unit area, related weather conditions (temperature and precipitation), geographical location, landscape, and syntaxonomy. The data were obtained from various, mostly European, pedoclimatic zones, and the majority of the data were available for plant species and communities present in Central Europe, especially in Poland, where research on floral resources has a long tradition. These data are representative of the whole continent and may be used as a reference for plant communities occurring on continents other than Europe since the database allows the consideration of differences in the production of resources by a single plant species growing in different communities. This dataset provides a unique opportunity to test hypotheses related to the functioning of food webs, nutrient cycling, plant ecology, and pollinator ecology and conservation.</p>
Data from: Vegetative phenologies of lianas and trees in two Neotropical forests with contrasting rainfall regimes
<ol> <li>Among tropical forests, lianas are predicted to have a growth advantage over trees during seasonal drought, with substantial implications for tree and forest dynamics. We tested the hypotheses that lianas maintain higher water status than trees during seasonal drought and that lianas maximize leaf cover to match high, dry-season light conditions while trees are more limited by moisture availability during the dry season.</li> <li>We monitored the seasonal dynamics of predawn and midday leaf water potentials and leaf phenology for branches of 16 liana and 16 tree species in the canopy of two lowland tropical forests with contrasting rainfall regimes in Panama.</li> <li>In a wet, weakly seasonal forest, lianas maintained higher water balance than trees and maximized their leaf cover during dry-season conditions, when light availability was high, while trees experienced drought stress. In a drier, strongly seasonal forest, lianas and trees displayed similar dry season reductions in leaf cover following strong decreases in soil water availability.</li> <li>Greater soil moisture availability and a higher capacity to maintain water status allow lianas to maintain the turgor potentials critical for plant growth in a wet and weakly seasonal forest but not in a dry and strongly seasonal forest.</li> </ol>
Data for 'Phenological shifts in a warming world affect physiology and life history in a damselfly'
<p>In this analysis we studied, in laboratory conditions, the impact of warming and hatching dates on key life history and physiological traits in a cannibalistic damselfly, <em>Ischnura</em> <em>elegans</em>. Larvae were reared in groups from hatching to emergence through one or two growth seasons, depending on the voltinism. Larvae were equally divided by hatching dates (early and late) and temperature treatment (current and warming). Early and late hatched groups were not mixed. This data set includes:</p> <ul> <li>survival until emergence and emergence success data</li> <li>development time (from hatching to emergence, in days)</li> <li>mass of adult insects</li> <li>growth rate (mass of adult insect/development time in days)</li> <li>protein content (μg of protein/μl of prepared homogenate)</li> <li>phenoloxidase activity (PO, value of activity curve slope)</li> <li>PO activity/protein (value of activity curve slope/(μg of protein/μl of prepared homogenate)</li> <li>insects voltinism (univoltine/semivoltine)</li> </ul>
Historical phenology data from Hough (1864) and datasheets for phenometric analyses
<p>This dataset contains a zip file of phenology data from Hough (1864) converted from tables in that source to a format usable for analyses. This dataset also contains the summary datasheet for a phenometric analysis for an in process manuscript, 'Phenological response to climatic change depends on seasonal warming velocity and species traits' by Robert Guralnick, Erin Grady, Theresa Crimmins, and Lindsay Campbell.</p>
ScienceDex guides
Understand access before you commit
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.