MARCO POLO – A new and simple tool for pollen-based stand-scale vegetation reconstruction. (March 2017)

• Record Type: Journal Article
• Authors: Mrotzek, Almut; Couwenberg, John; Theuerkauf, Martin; Joosten, Hans
• Is Part Of: Holocene. Volume 27:Number 3(2017)
• Page Start: 321
• Page End: 330
• Publication Date: 2017-03
• Subjects: forest community -- local pollen deposition -- pollen correction factors -- pollen productivity estimates -- quantitative vegetation reconstruction -- R-values -- surface samples; Geology, Stratigraphic -- Holocene -- Periodicals; Paleoclimatology -- Periodicals; 333.7
• DOI: 10.1177/0959683616660171
• Languages: English
• ISSNs: 0959-6836

Abstract

Hitherto, the Landscape Reconstruction Algorithm (LRA) has been the only truly quantitative approach to stand-scale palynology. However, the LRA requires information on pollen productivity and dispersal, which is not always available. The alternative approach MARCO POLO (MAnipulating pollen sums to ReCOnstruct POllen of Local Origin) presented here is solely based on pollen values and does not rely on a pollen dispersal function. In a stepwise fashion, MARCO POLO removes those pollen types from the pollen sum whose values are significantly higher than in a neighbouring large basin. The resulting regional pollen sum is free of the disturbing factor of (extra-)local pollen. Based on this sum, comparison with the pollen record from the large basin allows calculating sharp (extra-)local signals. Treating the (extra-)local pollen portion with representation factors ( R -values) then produces a quantitative reconstruction of the stand-scale vegetation composition. We tested MARCO POLO and the LRA on a dataset of pollen surface samples and forest vegetation relevés from northern Central Europe. Both approaches reconstruct the presence or absence of taxa at the stand scale within a small margin of error. Where observed cover was ⩾2%, both models always reconstructed presence, where modelled cover was ⩾2% the taxon was always present. Overall, both approaches perform well in reconstructing the cover of taxa within a 100-m radius. In our tests, MARCO POLO is slightly better at reconstructing cover values for more taxa. Although some model parameters evidently need revision, the simple correlative approach of MARCO POLO appears to perform at least as well as the complex LRA model.