Literature DB >> 29455297

Pan European Phenological database (PEP725): a single point of access for European data.

Barbara Templ1, Elisabeth Koch1, Kjell Bolmgren2, Markus Ungersböck1, Anita Paul1, Helfried Scheifinger3, This Rutishauser4, Montserrat Busto5, Frank-M Chmielewski6, Lenka Hájková7, Sabina Hodzić8, Frank Kaspar9, Barbara Pietragalla10, Ramiro Romero-Fresneda11, Anne Tolvanen12,13, Višnja Vučetič14, Kirsten Zimmermann9, Ana Zust15.   

Abstract

The Pan European Phenology (PEP) project is a European infrastructure to promote and facilitate phenological research, education, and environmental monitoring. The main objective is to maintain and develop a Pan European Phenological database (PEP725) with an open, unrestricted data access for science and education. PEP725 is the successor of the database developed through the COST action 725 "Establishing a European phenological data platform for climatological applications" working as a single access point for European-wide plant phenological data. So far, 32 European meteorological services and project partners from across Europe have joined and supplied data collected by volunteers from 1868 to the present for the PEP725 database. Most of the partners actively provide data on a regular basis. The database presently holds almost 12 million records, about 46 growing stages and 265 plant species (including cultivars), and can be accessed via http://www.pep725.eu/ . Users of the PEP725 database have studied a diversity of topics ranging from climate change impact, plant physiological question, phenological modeling, and remote sensing of vegetation to ecosystem productivity.

Keywords:  Citizen science; Climate change; Europe; Long-term data; Plant phenology

Mesh:

Year:  2018        PMID: 29455297     DOI: 10.1007/s00484-018-1512-8

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  13 in total

1.  The effect of urbanization on plant phenology depends on regional temperature.

Authors:  Daijiang Li; Brian J Stucky; John Deck; Benjamin Baiser; Robert P Guralnick
Journal:  Nat Ecol Evol       Date:  2019-11-11       Impact factor: 15.460

2.  Global warming reduces leaf-out and flowering synchrony among individuals.

Authors:  Constantin M Zohner; Lidong Mo; Susanne S Renner
Journal:  Elife       Date:  2018-11-12       Impact factor: 8.140

3.  Integrating herbarium specimen observations into global phenology data systems.

Authors:  Laura Brenskelle; Brian J Stucky; John Deck; Ramona Walls; Rob P Guralnick
Journal:  Appl Plant Sci       Date:  2019-03-07       Impact factor: 1.936

4.  Estimating flowering transition dates from status-based phenological observations: a test of methods.

Authors:  Shawn D Taylor
Journal:  PeerJ       Date:  2019-09-24       Impact factor: 2.984

5.  Widespread decline in winds delayed autumn foliar senescence over high latitudes.

Authors:  Chaoyang Wu; Jian Wang; Philippe Ciais; Josep Peñuelas; Xiaoyang Zhang; Oliver Sonnentag; Feng Tian; Xiaoyue Wang; Huanjiong Wang; Ronggao Liu; Yongshuo H Fu; Quansheng Ge
Journal:  Proc Natl Acad Sci U S A       Date:  2021-04-20       Impact factor: 11.205

6.  The International Phenological Garden network (1959 to 2021): its 131 gardens, cloned study species, data archiving, and future.

Authors:  Susanne S Renner; Frank-M Chmielewski
Journal:  Int J Biometeorol       Date:  2021-09-07       Impact factor: 3.787

7.  The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline.

Authors:  Allan Buras; Anja Rammig; Christian S Zang
Journal:  Front Plant Sci       Date:  2021-12-01       Impact factor: 5.753

8.  Predicting spring migration of two European amphibian species with plant phenology using citizen science data.

Authors:  Maria Peer; Daniel Dörler; Johann G Zaller; Helfried Scheifinger; Silke Schweiger; Gregor Laaha; Gernot Neuwirth; Thomas Hübner; Florian Heigl
Journal:  Sci Rep       Date:  2021-11-03       Impact factor: 4.379

9.  The Plant Phenology Ontology: A New Informatics Resource for Large-Scale Integration of Plant Phenology Data.

Authors:  Brian J Stucky; Rob Guralnick; John Deck; Ellen G Denny; Kjell Bolmgren; Ramona Walls
Journal:  Front Plant Sci       Date:  2018-05-01       Impact factor: 5.753

10.  Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.

Authors:  Rachel A Reeb; Naeem Aziz; Samuel M Lapp; Justin Kitzes; J Mason Heberling; Sara E Kuebbing
Journal:  Front Plant Sci       Date:  2022-01-17       Impact factor: 5.753

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