Tue, Aug 03, 2021:On Demand
Background/Question/Methods
Over the past 20 years, air quality has improved dramatically in the United States, both in terms of emission reductions and reductions in nitrogen and sulfur deposition from the atmosphere into ecosystems. However, pollutants generated by human activities continue to drift into protected ecosystems, with negative impacts on resident biota and human visitors. The National Park Service has adopted a number of different approaches to preserve and protect park ecosystems against air pollution, including identifying sensitive species and setting critical loads. Traditionally, this might have been accomplished by conducting a study within a park, and applying its research findings and recommendations within the park, or within nearby parks with similar climate and species. However, the scale of long-term national monitoring datasets like the US Forest Service’s Forest Inventory and Assessment (FIA) dataset have allowed the identification of sensitive species and critical loads at a national scale. The wealth of information, generated at different scales, presents a challenge in understanding the effects of air pollution within the boundaries of each of the 48 NPS Class I areas and more than 280 inventoried parks.
Results/Conclusions We will present examples of synthesizing air pollution dose-response data with other datasets to show how we are identifying risk of air pollution effects in parks which have not hosted primary research studies. Biological inventories within parks allow the identification of sensitive species and the direct application of critical loads. The use of spatially explicit land cover, ecological regions, or soils data will allow geographic inference, both in scaling down to identify particular regions of the park at high risk, and in scaling up to catch species common in the region that have not been listed in the park. The use of ethnobotanical data will allow cultural inference, connecting effects of air pollution upon organisms with human uses and values outside of the park’s conservation. Finally, relationships between species may allow phylogenetic inference, identifying species within parks that are closely related to known sensitive species but which have not themselves been tested for air pollution effects.
Results/Conclusions We will present examples of synthesizing air pollution dose-response data with other datasets to show how we are identifying risk of air pollution effects in parks which have not hosted primary research studies. Biological inventories within parks allow the identification of sensitive species and the direct application of critical loads. The use of spatially explicit land cover, ecological regions, or soils data will allow geographic inference, both in scaling down to identify particular regions of the park at high risk, and in scaling up to catch species common in the region that have not been listed in the park. The use of ethnobotanical data will allow cultural inference, connecting effects of air pollution upon organisms with human uses and values outside of the park’s conservation. Finally, relationships between species may allow phylogenetic inference, identifying species within parks that are closely related to known sensitive species but which have not themselves been tested for air pollution effects.