Quantitative estimation of bioclimatic parameters from presence/absence vegetation data in North America by the modern analog technique

The method of modern analogs is widely used to obtain estimates of past climatic conditions from paleobiological assemblages, and despite its frequent use, this method involved so-far untested assumptions. We applied four analog approaches to a continental-scale set of bioclimatic and plant-distribution presence/absence data for North America to assess how well this method works under near-optimal modern conditions. For each point on the grid, we calculated the similarity between its vegetation assemblage and those of all other points on the grid (excluding nearby points). The climate of the points with the most similar vegetation was used to estimate the climate at the target grid point. Estimates based the use of the Jaccard similarity coefficient had smaller errors than those based on the use of a new similarity coefficient, although the latter may be more robust because it does not assume that the “fossil” assemblage is complete. The results of these analyses indicate that presence/absence vegetation assemblages provide a valid basis for estimating bioclimates on the continental scale. However, the accuracy of the estimates is strongly tied to the number of species in the target assemblage, and the analog method is necessarily constrained to produce estimates that fall within the range of observed values. We applied the four modern analog approaches and the mutual overlap (or “mutual climatic range”) method to estimate bioclimatic conditions represented by the plant macrofossil assemblage from a packrat midden of Last Glacial Maximum age from southern Nevada. In general, the estimation approaches produced similar results in regard to moisture conditions, but there was a greater range of estimates for growing-degree days. Despite its limitations, the modern analog technique can provide paleoclimatic reconstructions that serve as the starting point to the interpretation of past climatic conditions.