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Bayesian Analysis of Climate Change Effects on Observed and Projected Airborne Levels of Birch Pollen.

ABSTRACT: A Bayesian framework is presented for modeling Effects of climate change on pollen indices such as annual birch pollen count, maximum daily birch pollen count, start date of birch pollen season and the date of maximum daily birch pollen count. Annual mean CO2 concentration, mean spring temperature and the corresponding pollen index of prior year were found to be statistically significant accounting for Effects of climate change on four pollen indices. Results suggest that annual productions and peak values from 2020 to 2100 under different scenarios will be 1.3-8.0 and 1.1-7.3 times higher respectively than the mean values for 2000, and start and peak dates will occur around two to four weeks earlier. These results have been partly confirmed by the available historical data. As a demonstration, the emission profiles in future years were generated by incorporating the predicted pollen indices into an existing emission model.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC3601922 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Bayesian Analysis of Climate Change Effects on Observed and Projected Airborne Levels of Birch Pollen.

Zhang Yong Y Isukapalli Sastry S Bielory Leonard L Georgopoulos Panos P

Atmospheric environment (Oxford, England : 1994) 20121112

A Bayesian framework is presented for modeling Effects of climate change on pollen indices such as annual birch pollen count, maximum daily birch pollen count, start date of birch pollen season and the date of maximum daily birch pollen count. Annual mean CO<sub>2</sub> concentration, mean spring temperature and the corresponding pollen index of prior year were found to be statistically significant accounting for Effects of climate change on four pollen indices. Results suggest that annual produ ...[more]

PMID: 23526049

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Project description:Global change has the potential to affect river flow conditions which are fundamental determinants of physical habitats. Predictions of the effects of flow alterations on aquatic biota have mostly been assessed based on species ecological traits (e.g., current preferences), which are difficult to link to quantitative discharge data. Alternatively, we used empirically derived predictive relationships for species' response to flow to assess the effect of flow alterations due to climate change in two contrasting central European river catchments. Predictive relationships were set up for 294 individual species based on (1) abundance data from 223 sampling sites in the Kinzig lower-mountainous catchment and 67 sites in the Treene lowland catchment, and (2) flow conditions at these sites described by five flow metrics quantifying the duration, frequency, magnitude, timing and rate of flow events using present-day gauging data. Species' abundances were predicted for three periods: (1) baseline (1998-2017), (2) horizon 2050 (2046-2065) and (3) horizon 2090 (2080-2099) based on these empirical relationships and using high-resolution modeled discharge data for the present and future climate conditions. We compared the differences in predicted abundances among periods for individual species at each site, where the percent change served as a proxy to assess the potential species responses to flow alterations. Climate change was predicted to most strongly affect the low-flow conditions, leading to decreased abundances of species up to -42%. Finally combining the response of all species over all metrics indicated increasing overall species assemblage responses in 98% of the studied river reaches in both projected horizons and were significantly larger in the lower-mountainous Kinzig compared to the lowland Treene catchment. Such quantitative analyses of freshwater taxa responses to flow alterations provide valuable tools for predicting potential climate-change impacts on species abundances and can be applied to any stressor, species, or region.

Dataset Information

Bayesian Analysis of Climate Change Effects on Observed and Projected Airborne Levels of Birch Pollen.

Publications

Bayesian Analysis of Climate Change Effects on Observed and Projected Airborne Levels of Birch Pollen.

Similar Datasets

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