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Representativeness of point‐wise phenological Betula data collected in different parts of Europe
Author(s) -
Siljamo Pilvi,
Sofiev Mikhail,
Ranta Hanna,
Linkosalo Tapio,
Kubin Eero,
Ahas Rein,
Genikhovich Eugene,
Jatczak Katarzyna,
Jato Victoria,
Nekovář Jiří,
Minin Alexander,
Severova Elena,
Shalaboda Valentina
Publication year - 2008
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/j.1466-8238.2008.00383.x
Subject(s) - representativeness heuristic , phenology , environmental science , weather station , climate change , physical geography , geography , climatology , selection (genetic algorithm) , ecoregion , statistics , ecology , meteorology , computer science , mathematics , biology , geology , artificial intelligence
Aim We examine issues of uncertainty regarding the spatial and temporal representativeness of phenological observations using a newly compiled Europe‐wide data base of phenological observations for Betula species. Location Europe. Methods A new data base was compiled from national phenological observations covering 15 European countries, with the longest observational periods exceeding several decades for some sites. From this, the spatial and temporal representativeness of phenological observations were evaluated via statistical analysis. Results The results showed that there was a significant and irreducible uncertainty related to the use of data of a single station, which varied from 3 to 8 days depending on the station location. In more continental and northern climatic zones the uncertainty was lower, probably due to faster spring‐time weather developments. In mild climatic conditions, the uncertainty of dates of the phenological phases registered by a single station exceeded 1 week. The considerable number of data allowed us to preliminarily estimate the features of some stations, marking them as ‘late’, ‘early’, ‘representative’ or ‘random’, depending on the dates reported by these sites and the corresponding regional means. Main conclusions The uncertainties discovered in single‐site phenological observations are significant for virtually any potential application. Possible approaches for handling the uncertainty problem are station pre‐averaging and spatial regularization of the data set, pre‐selection (down‐sampling) or changing the description of the phenomena from deterministic to probabilistic.