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Cited by 512

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Theory of quantitative reconstruction of vegetation II: all you need is LOVE

Sugita, Shinya·2007·The Holocene
DOI 10.1177/0959683607075838
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Abstract

This paper describes the LOVE (LOcal Vegetation Estimates) model for estimating local vegetation composition within the relevant source area of pollen. This model quantifies and then subtracts background pollen (ie, pollen coming from beyond the relevant source area) in order to arrive at a quantitative reconstruction of local vegetation. Parameters required for LOVE applications are pollen counts from target sites, the relevant source area of pollen of these sites, pollen productivity estimates and regional vegetation composition within 10 4 -10 5 km 2. Regional vegetation composition is obtained using fossil pollen from large sites (≥10 2 ha) with the REVEALS (Regional Estimates of VEgetation Abundance from Large Sites) model, the first step of the Landscape Reconstruction Algorithm (LRA) specifically designed for LOVE applications. POLLSCAPE simulations demonstrate that (1) regional vegetation composition can be used to predict background pollen at and beyond the relevant source area of pollen for given-sized basins, (2) LOVE with the LRA framework provides a robust and accurate estimate of local vegetation composition much better than vegetation reconstruction using pollen percentages alone and (3) although the relevant source area of pollen is difficult to estimate particularly in past landscapes, a proposed method using backward modelling of LOVE is effective to estimate the relevant source area in landscapes of unknown vegetation patchiness and heterogeneity. Thus, the LRA will also be useful to estimate indirectly changes in spatial structure of past vegetation and landscape caused by natural and anthropogenic forcing.

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Metadata

Authors
Sugita, S.
Venue
The Holocene
Publisher
SAGE Publications
Published
2007-02
Volume
17
Issue
2
Pages
243-257
ISSN
  • 0959-6836 (ISSN-L)
  • 1477-0911
DOI
10.1177/0959683607075838
OpenAlex
W2040709045
Cited by
512
References
30
FWCI
16.22
Type
journal article
Language
en
Open access
closed

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