Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology
Ovaskainen, Otso; Meyke, Evgeniy; Lo, Coong; Tikhonov, Gleb; del Mar Delgado, Maria; Roslin, Tomas; Gurarie, Eliezer; Abadonova, Marina; Abduraimov, Ozodbek; Adrianova, Olga; Akimova, Tatiana; Akkiev, Muzhigit; Ananin, Aleksandr; Andreeva, Elena; Andriychuk, Natalia; Antipin, Maxim; Arzamascev, Konstantin; Babina, Svetlana; Babushkin, Miroslav; Bakin, Oleg; Barabancova, Anna; Basilskaja, Inna; Belova, Nina; Belyaeva, Natalia; Bespalova, Tatjana; Bisikalova, Evgeniya; Bobretsov, Anatoly; Bobrov, Vladimir; Bobrovskyi, Vadim; Bochkareva, Elena Viktorovna; Bogdanov, Gennady; Bolshakov, Vladimir; Bondarchuk, Svetlana; Bukharova, Evgeniya; Butunina, Alena; Buyvolov, Yuri; Buyvolova, Anna; Bykov, Yuri; Chakhireva, Elena; Chashchina, Olga; Cherenkova, Nadezhda; Chistjakov, Sergej; Chuhontseva, Svetlana; Davydov, Evgeniy; Demchenko, Viktor; Diadicheva, Elena; Dobrolyubov, Aleksandr; Dostoyevskaya, Ludmila; Drovnina, Svetlana; Drozdova, Zoya
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3015100Utgivelsesdato
2020Metadata
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- Institutt for biologi [2596]
- Publikasjoner fra CRIStin - NTNU [38294]
Sammendrag
We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890–2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change.