Improving estimates of regional vegetation: Using pre-settlement vegetation data and variable wind speed to quantify pollen dispersal and source area

Abstract:

Pollen-based vegetation reconstructions are the primary source of information about spatio-temporal trends in vegetation dynamics at timescales of centuries to millennia. Pollen samples from individual lakes, bogs, and small hollows provide information about the vegetation within their respective source areas, which when mapped together generate vegetation reconstructions across spatial and temporal scales. Climate histories also can be reconstructed using empirical taxa-climate relationships. A fundamental need in these reconstructions is to estimate the pollen source area and account for the effects of intertaxonomic differences in pollen production and dispersal. Mechanistic pollen-vegetation models have continuously evolved for nearly a century, with recent advances culminating in the Landscape Reconstruction Algorithm. However, most applications of the LRA do not account for anisotropies in pollen source area introduced by atmospheric variations.

Given that wind direction is often anisotropic and variable we investigate the effects of wind speed, wind direction, and a simple treatment of wet versus dry deposition on vegetation reconstruction from pollen. We obtained long term estimates of dominant wind patterns using North American Regional Reanalysis (NARR) weather data for 1979-2012. However, because contemporary vegetation and pollen distributions have been heavily affected by Euro-American land use, we use pre-settlement (ca. 1810-1904) forest composition across the prairie-forest ecotone in the upper Midwestern United States and a dataset of settlement-era pollen samples compiled by the PalEON project to model pollen-vegetation relationships. Comparisons of our modelled results with those of a unidirectional, homogenous model shows a substantial effect of regionally varying winds on vegetation reconstructions. In addition to variable wind speed and direction, other atmospheric effects including precipitation and instability play an important role in pollen transport and deposition. By better accounting for these effects we can improve the predictive capacity of pollen-vegetation models and reduce uncertainty.