Dataset Information

Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology.

ABSTRACT: 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.

SUBMITTER: Ovaskainen O

PROVIDER: S-EPMC7012846 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology.

Ovaskainen Otso O Meyke Evgeniy E Lo Coong C Tikhonov Gleb G Delgado Maria Del Mar MDM Roslin Tomas T Gurarie Eliezer E Abadonova Marina M Abduraimov Ozodbek O Adrianova Olga O Akimova Tatiana T Akkiev Muzhigit M Ananin Aleksandr A Andreeva Elena E Andriychuk Natalia N Antipin Maxim M Arzamascev Konstantin K Babina Svetlana S Babushkin Miroslav M Bakin Oleg O Barabancova Anna A Basilskaja Inna I Belova Nina N Belyaeva Natalia N Bespalova Tatjana T Bisikalova Evgeniya E Bobretsov Anatoly A Bobrov Vladimir V Bobrovskyi Vadim V Bochkareva Elena E Bogdanov Gennady G Bolshakov Vladimir V Bondarchuk Svetlana S Bukharova Evgeniya E Butunina Alena A Buyvolov Yuri Y Buyvolova Anna A Bykov Yuri Y Chakhireva Elena E Chashchina Olga O Cherenkova Nadezhda N Chistjakov Sergej S Chuhontseva Svetlana S Davydov Evgeniy A EA Demchenko Viktor V Diadicheva Elena E Dobrolyubov Aleksandr A Dostoyevskaya Ludmila L Drovnina Svetlana S Drozdova Zoya Z Dubanaev Akynaly A Dubrovsky Yuriy Y Elsukov Sergey S Epova Lidia L Ermakova Olga S OS Ermakova Olga O Esengeldenova Aleksandra A Evstigneev Oleg O Fedchenko Irina I Fedotova Violetta V Filatova Tatiana T Gashev Sergey S Gavrilov Anatoliy A Gaydysh Irina I Golovcov Dmitrij D Goncharova Nadezhda N Gorbunova Elena E Gordeeva Tatyana T Grishchenko Vitaly V Gromyko Ludmila L Hohryakov Vladimir V Hritankov Alexander A Ignatenko Elena E Igosheva Svetlana S Ivanova Uliya U Ivanova Natalya N Kalinkin Yury Y Kaygorodova Evgeniya E Kazansky Fedor F Kiseleva Darya D Knorre Anastasia A Kolpashikov Leonid L Korobov Evgenii E Korolyova Helen H Korotkikh Natalia N Kosenkov Gennadiy G Kossenko Sergey S Kotlugalyamova Elvira E Kozlovsky Evgeny E Kozsheechkin Vladimir V Kozurak Alla A Kozyr Irina I Krasnopevtseva Aleksandra A Kruglikov Sergey S Kuberskaya Olga O Kudryavtsev Aleksey A Kulebyakina Elena E Kulsha Yuliia Y Kupriyanova Margarita M Kurbanbagamaev Murad M Kutenkov Anatoliy A Kutenkova Nadezhda N Kuyantseva Nadezhda N Kuznetsov Andrey A Larin Evgeniy E Lebedev Pavel P Litvinov Kirill K Luzhkova Natalia N Mahmudov Azizbek A Makovkina Lidiya L Mamontov Viktor V Mayorova Svetlana S Megalinskaja Irina I Meydus Artur A Minin Aleksandr A Mitrofanov Oleg O Motruk Mykhailo M Myslenkov Aleksandr A Nasonova Nina N Nemtseva Natalia N Nesterova Irina I Nezdoliy Tamara T Niroda Tatyana T Novikova Tatiana T Panicheva Darya D Pavlov Alexey A Pavlova Klara K Petrenko Polina P Podolski Sergei S Polikarpova Natalja N Polyanskaya Tatiana T Pospelov Igor I Pospelova Elena E Prokhorov Ilya I Prokosheva Irina I Puchnina Lyudmila L Putrashyk Ivan I Raiskaya Julia J Rozhkov Yuri Y Rozhkova Olga O Rudenko Marina M Rybnikova Irina I Rykova Svetlana S Sahnevich Miroslava M Samoylov Alexander A Sanko Valeri V Sapelnikova Inna I Sazonov Sergei S Selyunina Zoya Z Shalaeva Ksenia K Shashkov Maksim M Shcherbakov Anatoliy A Shevchyk Vasyl V Shubin Sergej S Shujskaja Elena E Sibgatullin Rustam R Sikkila Natalia N Sitnikova Elena E Sivkov Andrei A Skok Nataliya N Skorokhodova Svetlana S Smirnova Elena E Sokolova Galina G Sopin Vladimir V Spasovski Yurii Y Stepanov Sergei S Stratiy Vitalіy V Strekalovskaya Violetta V Sukhov Alexander A Suleymanova Guzalya G Sultangareeva Lilija L Teleganova Viktorija V Teplov Viktor V Teplova Valentina V Tertitsa Tatiana T Timoshkin Vladislav V Tirski Dmitry D Tolmachev Andrej A Tomilin Aleksey A Tselishcheva Ludmila L Turgunov Mirabdulla M Tyukh Yurij Y Vladimir Van V Vargot Elena E Vasin Aleksander A Vasina Aleksandra A Vekliuk Anatoliy A Vetchinnikova Lidia L Vinogradov Vladislav V Volodchenkov Nikolay N Voloshina Inna I Xoliqov Tura T Yablonovska-Grishchenko Eugenia E Yakovlev Vladimir V Yakovleva Marina M Yantser Oksana O Yarema Yurij Y Zahvatov Andrey A Zakharov Valery V Zelenetskiy Nicolay N Zheltukhin Anatolii A Zubina Tatyana T Kurhinen Juri J

Scientific data 20200211 1

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 ...[more]

PMID: 32047153

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Project description:Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science USA National Phenology Network (USA-NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both USA-NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for USA-NPN data had the lowest error when using models built from the USA-NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross-scale model comparison is likely due to variation in phenological requirements within species. Models using the USA-NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if USA-NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale USA-NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.

Project description:BackgroundAn accurate understanding of the current state of end-of-life care is important for healthcare planning. The objectives of this study were to examine the trajectories of end-of-life medical and long-term care expenditures and associated factors.MethodsThis was a retrospective longitudinal study using a large-scale linked database of medical and long-term care claims-National Health Insurance, Advanced Elderly Medical Insurance, and long-term care insurance-covering Prefecture A in Japan. Patients aged ≥70 years who died between April 1, 2016, and March 31, 2017, were included (N = 16,084 patients; mean age = 85.1 ± 7.5 years; 7804 men (48.5%) and 8280 women (51.5%)). The outcome measures were medical expenditures (inpatient, outpatient, and prescription), long-term care expenditures, and total healthcare expenditures (the sum of medical and long-term care expenditures) during the 60 months before the date of death. We calculated each patient's monthly medical and long-term care expenditures for 60 months before the date of death and applied group-based trajectory modeling to identify distinct trajectories. Factors associated with spending trajectories were examined via multinomial logistic regression analyses. Explanatory variables included age, sex, diseases, and the medical services used.ResultsWe identified six distinct spending trajectories for the total healthcare expenditures: high persistent (45.6%), medium-to-high persistent (26.1%), early rise then high persistent (9.8%), late rise (6.4%), low persistent then very late rise (i.e., when spending starts increasing later than "late rise"; 6.4%), and progressive increase (5.7%). Factors associated with the high-persistent trajectory were chronic illnesses, various organ failures, neurodegenerative diseases, fractures, and tube feeding. The trajectory pattern of medical expenditures was similar to that of total healthcare expenditures; however, a different pattern was seen for long-term care expenditures.ConclusionsRegarding combined medical and long-term care spending of the last 5 years, most patients belonged to a pattern in which the healthcare expenditures remained high, and a combination of multiple factors contributed to these patterns. This finding can offer healthcare providers a longer-term perspective on end-of-life care.

Dataset Information

Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology.

Publications

Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology.

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