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Discussion on temperature reconstruction with sediment core data in Ilvonen et al .
Author(s) -
Li Bo,
Barboza Luis,
Tingley Martin,
Viens Frederi
Publication year - 2016
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2399
Subject(s) - categorical variable , sediment core , proxy (statistics) , pollen , computer science , physical geography , econometrics , geology , sediment , paleontology , mathematics , geography , machine learning , ecology , biology
We congratulate the authors on an important contribution to temperature reconstruction methodology applicable to pollen data. Pollen data is more challenging to model than other climate proxies, such as measurements on tree rings, because of the categorical nature of pollen assemblage data and the dating uncertainty inherent to any sedimentary proxy. The authors are commended on developing a reconstruction methodology that incorporates Bchron and Bummer. In this discussion, we discuss several ideas prompted by our reading of this interesting and novel paper. Copyright © 2016 John Wiley & Sons, Ltd.