@article{maudlin_mcneal_dinon-aldridge_davis_boyles_atkins_2020, title={Website Usability Differences between Males and Females: An Eye-Tracking Evaluation of a Climate Decision Support System}, volume={12}, ISSN={["1948-8335"]}, DOI={10.1175/WCAS-D-18-0127.1}, abstractNote={ABSTRACTDecision support systems—collections of related information located in a central place to be used for decision-making—can be used as platforms from which climate information can be shared with decision-makers. Unfortunately, these tools are not often evaluated, meaning developers do not know how useful or usable their products are. In this study, a web-based climate decision support system (DSS) for foresters in the southeastern United States was evaluated by using eye-tracking technology. The initial study design was exploratory and focused on assessing usability concerns within the website. Results showed differences between male and female forestry experts in their eye-tracking behavior and in their success with completing tasks and answering questions related to the climate information presented in the DSS. A follow-up study, using undergraduate students from a large university in the southeastern United States, aimed to determine whether similar gender differences existed and could be detected and, if so, whether the cause(s) could be determined. The second evaluation, similar to the first, showed that males and females focused their attention on different aspects of the website; males focused more on the maps depicting climate information while females focused more on other aspects of the website (e.g., text, search bars, and color bars). DSS developers should consider the possibility of gender differences when designing a web-based DSS and include website features that draw user attention to important DSS elements to effectively support various populations of users.}, number={1}, journal={WEATHER CLIMATE AND SOCIETY}, author={Maudlin, Lindsay C. and McNeal, Karen S. and Dinon-Aldridge, Heather and Davis, Corey and Boyles, Ryan and Atkins, Rachel M.}, year={2020}, month={Jan}, pages={183–192} }
 @article{duan_sun_mcnulty_caldwell_cohen_sun_aldridge_zhou_zhang_zhang_2017, title={Future shift of the relative roles of precipitation and temperature in controlling annual runoff in the conterminous United States}, volume={21}, number={11}, journal={Hydrology and Earth System Sciences}, author={Duan, K. and Sun, G. and McNulty, S. G. and Caldwell, P. V. and Cohen, E. C. and Sun, S. L. and Aldridge, H. D. and Zhou, D. C. and Zhang, L. X. and Zhang, Y.}, year={2017}, pages={5517–5529} }
 @article{thomas_brooks_jersild_ward_wynne_albaugh_dinon-aldridge_burkhart_domec_fox_et al._2017, title={Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments}, volume={14}, ISSN={["1726-4189"]}, DOI={10.5194/bg-14-3525-2017}, abstractNote={Abstract. Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6  ×  105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.
                    }, number={14}, journal={BIOGEOSCIENCES}, author={Thomas, R. Quinn and Brooks, Evan B. and Jersild, Annika L. and Ward, Eric J. and Wynne, Randolph H. and Albaugh, Timothy J. and Dinon-Aldridge, Heather and Burkhart, Harold E. and Domec, Jean-Christophe and Fox, Thomas R. and et al.}, year={2017}, month={Jul}, pages={3525–3547} }
 @article{duan_sun_sun_caldwell_cohen_mcnulty_aldridge_zhang_2016, title={Divergence of ecosystem services in US National Forests and Grasslands under a changing climate}, volume={6}, ISSN={["2045-2322"]}, DOI={10.1038/srep24441}, abstractNote={AbstractThe 170 National Forests and Grasslands (NFs) in the conterminous United States are public lands that provide important ecosystem services such as clean water and timber supply to the American people. This study investigates the potential impacts of climate change on two key ecosystem functions (i.e., water yield and ecosystem productivity) using the most recent climate projections derived from 20 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 5 (CMIP5). We find that future climate change may result in a significant reduction in water yield but an increase in ecosystem productivity in NFs. On average, gross ecosystem productivity is projected to increase by 76 ~ 229 g C m−2 yr−1 (8% ~ 24%) while water yield is projected to decrease by 18 ~ 31 mm yr−1 (4% ~ 7%) by 2100 as a result of the combination of increased air temperature (+1.8 ~ +5.2 °C) and precipitation (+17 ~ +51 mm yr−1). The notable divergence in ecosystem services of water supply and carbon sequestration is expected to intensify under higher greenhouse gas emission and associated climate change in the future, posing greater challenges to managing NFs for both ecosystem services.}, journal={SCIENTIFIC REPORTS}, author={Duan, Kai and Sun, Ge and Sun, Shanlei and Caldwell, Peter V. and Cohen, Erika C. and McNulty, Steven G. and Aldridge, Heather D. and Zhang, Yang}, year={2016}, month={Apr} }
 @article{templeton_shane perkins_aldridge_bridges_lassiter_2014, title={Usefulness and uses of climate forecasts for agricultural extension in South Carolina, USA}, volume={14}, ISSN={["1436-378X"]}, DOI={10.1007/s10113-013-0522-7}, number={2}, journal={REGIONAL ENVIRONMENTAL CHANGE}, author={Templeton, Scott R. and Shane Perkins, M. and Aldridge, Heather Dinon and Bridges, William C., Jr. and Lassiter, Bridget Robinson}, year={2014}, month={Apr}, pages={645–655} }