BUMPER v1.0: a Bayesian user-friendly model for palaeo-environmental reconstruction
We describe the Bayesian user-friendly model for palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast,...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-02-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/10/483/2017/gmd-10-483-2017.pdf |
| Summary: | We describe the Bayesian user-friendly model for
palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function
for inferring past climate and other environmental variables from
microfossil assemblages. BUMPER is fully self-calibrating, straightforward
to apply, and computationally fast, requiring ∼ 2 s to
build a 100-taxon model from a 100-site training set on a standard personal
computer. We apply the model's probabilistic framework to generate thousands
of artificial training sets under ideal assumptions. We then use these to
demonstrate the sensitivity of reconstructions to the characteristics of the
training set, considering assemblage richness, taxon tolerances, and the
number of training sites. We find that a useful guideline for the size of a
training set is to provide, on average, at least 10 samples of each taxon.
We demonstrate general applicability to real data, considering three
different organism types (chironomids, diatoms, pollen) and different
reconstructed variables. An identically configured model is used in each
application, the only change being the input files that provide the
training-set environment and taxon-count data. The performance of BUMPER is
shown to be comparable with weighted average partial least squares (WAPLS)
in each case. Additional artificial datasets are constructed with similar
characteristics to the real data, and these are used to explore the reasons
for the differing performances of the different training sets. |
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| ISSN: | 1991-959X 1991-9603 |