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1,662 results for “Phenology”
Landscape Flowering Phenology Field Data for Sites in the Vicinity of Crested Butte, CO
This dataset represents field observations of reproductive development (flowering phenology) in 135 species of flowering plants collected at 12 field sites in the vicinity of Crested Butte, Colorado starting in 2019. Sites were visited approximately weekly from early May until early August, and all species in flower were recorded in 25 segments along a 50m transect at each site, and species were recorded if they were within 1m of either side of the transect. Datasets included in this package are 1) cleaned field observations of flowering phenology, 2) taxonomic identity of all recorded species, 3) spatial data representing the location of the center of each transect segment, and 4) spatial data representing the segment polygons.
Aquatic and terrestrial insect activity phenology with trap collections at the Andrews Experimental Forest, 2009-2014
This study was designed to evaluate the influence of microclimatic heterogeneity, associated with complex terrain, on phenology and to evaluate potential trophic responses to scenarios of climate change, disturbance and land use. We focus on a simplified model trophic system involving vascular plants, terrestrial and aquatic insects, and migratory neotropical and resident birds. The model trophic system is interesting because the phenologies of different components in the model system are independent (cued by various abiotic drivers) and dependent (due to trophic interactions), potentially leading to complex system behaviors. Plant and poiklothermic animal (ex. invertebrates) phenologies are highly temperature dependent. Phenologies of terrestrial plants and invertebrates would therefore likely exhibit wide spatial and temporal variation across the landscape in response to temperature variation associated with elevational differences, cold air drainages patterns, and temperature inversions. Aquatic invertebrate phenologies are also tied to temperature, but stream temperatures are influenced by different factors than those driving air temperatures and they may be less sensitive to complex terrain. For the invertebrate part of this study, we are examining spring-time (April through June) flying (terrestrial and adult aquatic) insect activity and adult aquatic insect emergence across a range of sites in the HJ Andrews Experimental Forest. Flying insect activity will be assessed using malaise traps deployed at 16 sites ranging from 450m to over 1300m in elevation, and with a variety of forest stand ages and slope aspects. Emerging aquatic insects will be collected with emergence traps in six 1st to 2nd order streams ranging from 450m to 1000m in elevation, and differing in water source (spring vs run-off) and surrounding forest age. Insects from malaise traps will be identified to varying levels from order to genus, depending on the group and available keys. Adult aquat
Stream temperature at core phenology stream sites in the Andrews Experimental Forest, 2009 - 2014
The H.J Andrews phenology research included this study of stream temperatures at six headwater streams where springtime emergence of stream macroinvertebrates were collected. Streams were selected across an elevational gradient and with young riparian forests as well as old growth riparian forests. One site was a stream that originated in a cold water spring and the site was a stream that drys late in the summer. Stream temperature loggers were placed in protective housings and stream temperatures collected year-round starting May 2009-July 2014. Data were evaluated for quality control and missing periods were filled. These data document the thermal environment that stream macroinvertebrates experience and that influence their timing of emergence (database SA025).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Phenology of Dominant Plant Species I - Bud burst and Senescence 2013-2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. In this dataset we specifically ask, how does warming of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Phenology of Dominant Plant Species I - Bud burst and Senescence 2013-2021
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. In this dataset we specifically ask, how does warming and drying of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Phenology of Dominant Plant Species II - Berry Production 2013-2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. In this dataset we specifically ask, how does warming of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Phenology of Dominant Plant Species III - Flowering Date 2013-2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. In this dataset we specifically ask, how does warming of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Phenology of Dominant Plant Species II - Berry Production 2013-2021
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. In this dataset we specifically ask, how does warming and drying of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Phenology of Dominant Plant Species III - Flowering Date 2013-2021
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. In this dataset we specifically ask, how does warming and drying of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
Plant phenology observations at 15 sites at Jornada Basin LTER, 1992-ongoing
This ongoing data package, which began in 1992, contains monthly plant phenological observations by species at the 15 Net Primary Production (NPP) study sites at the Jornada Experimental Range (JER) or Chihuahuan Desert Rangeland Research Center (CDRRC) in Dona Ana County, New Mexico, USA. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. A transect at each site is monitored monthly to assess the following phenological stages for each species: dormancy, non-reproductive status, budding, flowering, and fruiting. Sampling methods in the first years of the study monitored all plant species present but have been changed to focus on specific species at each site.
PPH01 Phenology of selected plant species at Konza Prairie
Twenty-nine selected species of grasses, forbs, and woody vegetation characteristic of a variety of habitats on Konza Prairie are used for phenological measurements. These species are observed weekly for the entire growing season and changes in their phenological states are recorded. The following phenological states are used for this survey: (1) initiation of growth, (2) first anthesis, (3) duration of anthesis, (4) fruits mature, (5) leaves more than 90% dry.
LFDP phenology plot seedlings-16 ha plot
These data on temporal and spatial patterns of seedling recruitment in conjunction with data on flower and seed rain production allow tests of hypotheses concerning causes of inter-annual variation and roles of different mechanisms in facilitating species coexistence. The data are being used to, among other things 1) quantify seasonal and inter-annual variation in reproductive output in tropical plant populations and communities; 2) analyze relationships between reproduction and a variety of hypothesized local and regional climate drivers; 3) evaluate evidence for long-term directional trends which could reflect responses to anthropogenic global change; 4) quantify spatiotemporal variation in seed arrival and seedling recruitment of individual species; 5) analyze associated evidence of recruitment limitation, life history trade-offs, density dependent recruitment and regeneration niche partitioning; and 6) evaluate how these processes interact with inter-annual variation in climate and plant reproduction. 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.
Phenologies of the Tabonuco Forest trees and shrubs
These data are being used to, among other things, (1) determine the seasonality of flowering and fruiting in Tabonuco forest and test hypotheses concerning the causation of seasonality (or lack thereof), (2) test the effect of annual variation in rainfall and other climatic variables on seed and fruit production of individual species, and (3) compare the relative dispersal of species on the Luquillo Forest Dynamics Plot by applying information on the spatial distribution of canopy trees to the data on seed and fruit fall. These data also serve as background information on the flowering and fruiting of individual species. 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.
N and P fertilization experiment plant phenology data for South of saddle, 1993-1994
The effects of nitrogen, phosphorus, and nitrogen+phosphorus fertilization on phenology of alpine plants was examined. This was a preliminary study to determine if the phenologies of dry meadow plants were affected by nutrient amendments, and if they were, which species responded most to the treatments. Times of emergence, senescence, and other key vegetative and reproductive "events" were recorded.
Above and below ground phenology for the Niwot Ridge sensor node array, 2021 - 2022.
The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. We deployed root in-growth cores in 4 locations in Niwot Ridge’s sensor node array as part of a multi-site study of above- and below-ground tundra phenology.
SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024
As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.
Phenological metrics for Protected Area "BayerischerWald", MODIS aqua 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 "Donana", MODIS terra tile h17v05
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 "Samaria", MODIS terra tile h19v05
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 "SierraNevadaEcosystem", MODIS aqua tile h17v05
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;
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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)
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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.