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17 results for “Phenology analysis”
CARDAMOM Phenology Study: Dataset and Analysis Code
<p>This repository stores the input data, output data and analysis code for the following publication:</p> <p>Norton, A. J., Bloom, A. A., Parazoo, N. C., Levine, P. A., Ma, S., Braghiere, R. K., and Smallman, L. T.: Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1265, 2022.</p>
Individual repeatability of avian migration phenology: a systematic review and meta-analysis
<p>Changes in phenology and distribution are being widely reported for many migratory species in response to shifting environmental conditions. Understanding these changes and the situations in which they occur can be aided by understanding consistent individual differences in phenology and distribution and the situations in which consistency varies in strength or detectability.</p> <p>Studies tracking the same individuals over consecutive years are increasingly reporting migratory timings to be a repeatable trait, suggesting that flexible individual responses to environmental conditions may contribute little to population-level changes in phenology and distribution. However, how this varies across species and sexes, across the annual cycle and in relation to study (tracking method, study design) and/or ecosystem characteristics is not yet clear.</p> <p>Here, we take advantage of the growing number of publications in movement ecology to perform a phylogenetic multilevel meta-analysis of repeatability estimates for avian migratory timings to investigate these questions. Of 2,433 reviewed studies, 54 contained suitable information for meta-analysis, resulting in 177 effect sizes from 47 species.</p> <p>Individual repeatability of avian migratory timings averaged 0.414 (95% confidence interval: 0.3–0.5) across landbirds, waterbirds and seabirds, suggesting consistent individual differences in migratory timings is a common feature of migratory systems. Timing of departure from the non-breeding grounds was more repeatable than timings of arrival at or departure from breeding grounds, suggesting that conditions encountered on migratory journeys and outcome of breeding attempts can influence individual variation.</p> <p>Population-level shifts in phenology could arise through individual timings changing with environmental conditions and/or through shifts in the numbers of individuals with different timings. Our findings suggest that, in addition to identifying the conditions associated with individual variation in phenology, exploring the causes of between-individual variation will be key in predicting future rates and directions of changes in migratory timings. We, therefore, encourage researchers to report the within- and between- individual variance components underpinning the reported repeatability estimates to aid interpretation of migration behaviour. In addition, the lack of studies in the tropics means that levels of repeatability in less strongly seasonal environments are not yet clear.</p>
Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"
<p>Submitted forecasts for the 2021 Ecological Forecasting Initiative NEON Phenology Forecast Challenge and the analysis code for the accompanying manuscript. </p>
Individual repeatability of avian migration phenology: a systematic review and meta-analysis
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Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: an annotated dataset of reproductive organs in Strepanthus herbarium specimens
<p>This dataset contains annotations of 31 herbarium specimens of <em>Streptanhus tortuosus Kellogg</em> for which we have we carefully and manually drew and annotated the contours of four reproductive organs: “bud”, “flower”, “immature fruit” and “mature fruit”.</p> <p>The dataset can be used to assess the ability of automated methods to count and detect precisely the shapes of these reproductive organs, with a view to conducting phenological studies.</p> <p>The annotations are formatted in accordance with the COCO data format, a usual format for object detection tasks in the field of Computer Vision. The annotations are divided into two files:</p> <ul> <li>train_21_full_masks.json contains the mask coordinates and labels of 21 herbarium sheets that can be used for training models</li> <li>test_10_full_masks.json contains the mask coordinates and labels of 10 other herbarium that can be used as a groundtruth file for evaluating the predictions, typically with the COCO evaluation scripts (<a href="https://github.com/cocodataset/cocoapi">https://github.com/cocodataset/cocoapi</a>)</li> </ul> <p>Please refer to the following publication for a first assessment of this dataset with a Mask-RCNN approach:</p> <p><em>H. Goëau, A. Mora-Fallas, J. Champ, N. Love, S. Mazer, E. Mata-Montero, A. Joly, P. Bonnet. </em>2020. New fine-grained method for automated visual analysis of herbarium specimens: a case study for phenological data extraction. <em>Applications in Plant Sciences </em></p> <p> </p> <p> </p> <p> </p> <p> </p>
Fig. 2 in Chorological and Phenological Analysis of the Water Beetle Fauna (Coleoptera: Adephaga and Polyphaga) of Northern Tunisia
Fig. 2. Proportions of the water beetle fauna of northern Tunisia in four distributional categories.
Fig. 1 in Chorological and Phenological Analysis of the Water Beetle Fauna (Coleoptera: Adephaga and Polyphaga) of Northern Tunisia
Fig. 1. Map of the study area showing location of the study sites and principal regions in northern Tunisia.
FIG. 4 in Staggered-Entry Analysis of Breeding Phenology and Occupancy Dynamics of Arizona Toads from Historically Occupied Habitats of New Mexico, USA
FIG. 4. Estimated detection probability from the simple multi-season model fitted with calling data from historical and control sites, based on the relationship between detection and Julian date from the simple multi-season occupancy model. Julian values represent only the values present in our data set and range from 1 March to 30 May (Julian days 60–150).
FIG. 3 in Staggered-Entry Analysis of Breeding Phenology and Occupancy Dynamics of Arizona Toads from Historically Occupied Habitats of New Mexico, USA
FIG. 3. Estimated probabilities of yearly occupancy (y-axis; 6 95% CI) from the historically occupied (orange) and control (blue) sites for the (A) staggered-entry model (SE) and the (B) simple multi-season (SMS) model. Dotted error bars represent years when surveys were not conducted.
FIG. 2 in Staggered-Entry Analysis of Breeding Phenology and Occupancy Dynamics of Arizona Toads from Historically Occupied Habitats of New Mexico, USA
FIG. 2. Estimated probabilities of Arizona Toads entering sites between subsequent surveys (orange), given that they have not previously entered, based on the estimated relationship between entry and Julian date. Estimated probabilities of Arizona Toad departing sites between subsequent surveys (blue), given that they are already present, based on the estimated relationship between departure and Julian date. Yellow points show the probabilities that toads were available to be detected for each day. Julian values range from 1 March to 30 May.
FIG. 1 in Staggered-Entry Analysis of Breeding Phenology and Occupancy Dynamics of Arizona Toads from Historically Occupied Habitats of New Mexico, USA
FIG. 1. Study area in southwestern New Mexico showing historically occupied (n ¼ 86; red circles) and control (n ¼ 59; blue triangles) waterbodies sampled during the 2013–2016 and 2019 breeding seasons for the Arizona Toads (Anaxyrus microscaphus). Filled shapes represent sites where we detected toads at least once, and open shapes represent sites where we never detected toads. Basemap by Stamen Design, under CC BY 3.0.
Data from: When is the best time to flower and disperse? a comparative analysis of plant reproductive phenology in the Mediterranean
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Fig. 3 in Chorological and Phenological Analysis of the Water Beetle Fauna (Coleoptera: Adephaga and Polyphaga) of Northern Tunisia
Fig. 3. Seasonal profile of phenological groups of water beetles from northern Tunisia.
Snow phenology extraction, trend analysis, and M–K test for Tibetan plateau
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Data from: Responses of plant phenology to nitrogen addition: a meta-analysis
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Data from: Temporal shifts and temperature sensitivity of avian spring migratory phenology: a phylogenetic meta-analysis
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Dataset for the paper "Coupled effects of solar illumination and phenology on vegetation index determination: an analysis over the Amazonian forests using the SuperDove satellite constellation"
<p>Dataset for the paper:</p> <p><strong>Galvão, L.S.; Petri, C.A.; Dalagnol, R. 2023. Coupled effects of solar illumination and phenology on vegetation index determination: an analysis over the Amazonian forests using the SuperDove satellite constellation. <em>Submitted to GIScience & Remote Sensing</em>.</strong></p> <p> </p> <p>About the data:</p> <p>Attached you will find the Worksheet_SD_Amz.xls file containing the data used for the analysis, The file contains the data used to obtain the relationships between vegetation indices and solar angles for each of the 15 sites studied in the Amazon.</p> <ul> <li>EVI</li> <li>GRND</li> <li>MPRI</li> <li>NDVI</li> <li>RedEdge</li> <li>SZA</li> <li>SAA</li> </ul>
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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
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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.