Amazonian pollen assemblages reflect biogeographic gradients and forest cover

Aim: Pollen assemblages are commonly used to reconstruct past climates yet have not yet been used to reconstruct past human activities, including deforestation. We aim to assess (i) how pollen assemblages vary across biogeographic and environmental gradients, (ii) the source area of pollen assemblages from lake sediment samples and (iii) which pollen taxa can best be used to quantify deforested landscapes. Location: Amazonia. Taxon: Plantae. Methods: Pollen assemblages (N = 65) from mud- water interface samples (representing modern conditions) of lake sediment cores were compared with modern gradients of temperature, precipitation and elevation. Pollen assemblages were also compared with local- scale estimates of forest cover at 1, 2, 5, 10, 20 and 40 km buffers around each lake. Results: Over 250 pollen types were identified in the samples, and pollen assemblages were able to accurately differentiate biogeographic regions across the basin, corresponding with gradients in temperature and precipitation. Poaceae percentages were the best predictor of deforestation, and had a significant negative relationship with forest cover estimates. These relationships were strongest for the 1 km buffer area, weakening as buffer sizes increased. Main conclusions: The diverse Amazonian pollen assemblages strongly reflect environmental gradients, and percentages of Poaceae best reflect local- scale variability in forest cover. Our results of modern pollen- landscape relationships can be used to provide a foundation for quantitative reconstructions of climate and deforestation in Amazonia.

Automated summary

This study examines how pollen assemblages vary across biogeographic and environmental gradients in Amazonia. The results show that pollen assemblages accurately differentiate biogeographic regions and reflect gradients in temperature and precipitation. Poaceae percentages are the best predictor of deforestation and have a significant negative relationship with forest cover estimates.