Peter K. Van de Water

Leaf cellulose δD and δ18O trends with elevation differ in direction among co-occurring, semiarid plant species

Abstract The potential to reconstruct paleoclimate from analyses of stable isotopes in fossil leaf cellulose could be enhanced by adequate calibration. This potential is likely to be particularly great in mid-latitude deserts, where a rich store of fossil leaves is available from rodent middens. Trends in δD and δ 18 O of leaf cellulose were examined for three species growing across climatic gradients caused by elevation and slope aspect in southeastern Utah, USA. The species differed in morphology ( Pinus edulis vs. Yucca glauca ), photosynthetic pathway (C 3 Y. glauca vs. CAM Yucca baccata ) or both ( P. edulis vs. Y. baccata ). The δD LCN (leaf cellulose nitrate) and δ 18 O LC (leaf cellulose) values of P. edulis decreased with elevation. Stem water δD values either increased (in spring) or did not change with elevation (in summer). Needle water δD values usually decreased with elevation and differed greatly with leaf age. These results suggest that δ cellulose values of P. edulis record the effects of climate on the isotopic composition of leaf water but not climate effects on meteoric water. In contrast to P. edulis , δD LCN values of Y. glauca increased with elevation. The δ 18 O LC values of Y. glauca also increased with elevation but less significantly and only on south-facing slopes. The δ cellulose values in both P. edulis and Y. glauca were most significantly related to changes in temperature, although temperature and precipitation were negatively correlated in the study area. Where all three species co-occurred, their δD LCN values differed but their δ 18 O LC values were the same. The disparity in δD LCN between Y. baccata and the other species corresponds to differences in biochemical fractionations associated with photosynthetic pathway. Biochemical fractionations may also contribute to differences between the two C 3 species. Knowledge of factors affecting responses of individual plant species to environment may be required to infer climate from δD LCN and δ 18 O LC .

Pollen count forecasting

Pollen forecasting is becoming increasingly important to allergists as an adjunct to effective patient care. Forecasts allow patients to avoid exposure to high pollen levels and prompt them to take prophylactic medication and to plan outdoor activities for periods of low pollen levels. Investigators are making progress in developing effective models for daily and seasonal forecasts for important pollen allergens; however, current models are limited to specific geographic areas. Models for the onset of the season for spring tree pollen are based on the chilling and heat units that are required before flowering can occur. Models for pollen season severity are based on regression analysis of preseason meteorologic conditions, and models for daily forecasts are based on the normal pollen curve and responses to day-to-day meteorologic conditions. When winds are favorable, long-distance transport can introduce allergenic pollen types into a local area. The Mountain Cedar Pollen Forecasting model, which combines day-to-day release forecasts at source areas and dispersion forecasts to downwind areas, has been reasonablly successful over the past 4 years. All pollen forecasting models are dependent on accurate meteorologic forecasts, and pollen forecasting will become more accurate as meteorologic forecasts improve.