Browsing by Author "Poole, Geoffrey C."

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Characters matter: How narratives shape affective responses to risk communication
(Public Library of Science, 2019-12) Shanahan, Elizabeth A.; Reinhold, Ann Marie; Raile, Eric D.; Poole, Geoffrey C.; Ready, Richard C.; Izurieta, Clemente; McEvoy, Jamie; Bergmann, Nicolas T.; King, Henry
Introduction. Whereas scientists depend on the language of probability to relay information about hazards, risk communication may be more effective when embedding scientific information in narratives. The persuasive power of narratives is theorized to reside, in part, in narrative transportation. Purpose. This study seeks to advance the science of stories in risk communication by measuring real-time affective responses as a proxy indicator for narrative transportation during science messages that present scientific information in the context of narrative. Methods. This study employed a within-subjects design in which participants (n = 90) were exposed to eight science messages regarding flood risk. Conventional science messages using probability and certainty language represented two conditions. The remaining six conditions were narrative science messages that embedded the two conventional science messages within three story forms that manipulated the narrative mechanism of character selection. Informed by the Narrative Policy Framework, the characters portrayed in the narrative science messages were hero, victim, and victim-to-hero. Natural language processing techniques were applied to identify and rank hero and victim vocabularies from 45 resident interviews conducted in the study area; the resulting classified vocabulary was used to build each of the three story types. Affective response data were collected over 12 group sessions across three flood-prone communities in Montana. Dial response technology was used to capture continuous, second-by-second recording of participants’ affective responses while listening to each of the eight science messages. Message order was randomized across sessions. ANOVA and three linear mixed-effects models were estimated to test our predictions. Results. First, both probabilistic and certainty science language evoked negative affective responses with no statistical differences between them. Second, narrative science messages were associated with greater variance in affective responses than conventional science messages. Third, when characters are in action, variation in the narrative mechanism of character selection leads to significantly different affective responses. Hero and victim-to-hero characters elicit positive affective responses, while victim characters produce a slightly negative response. Conclusions// In risk communication, characters matter in audience experience of narrative transportation as measured by affective responses.
A coupled metabolic-hydraulic model and calibration scheme for estimating whole-river metabolism during dynamic flow conditions
(2017-09) Payn, Robert A.; Hall, R. O. Jr.; Kennedy, Theodore A.; Poole, Geoffrey C.; Marshall, L. A.
Conventional methods for estimating whole‐stream metabolic rates from measured dissolved oxygen dynamics do not account for the variation in solute transport times created by dynamic flow conditions. Changes in flow at hourly time scales are common downstream of hydroelectric dams (i.e., hydropeaking), and hydrologic limitations of conventional metabolic models have resulted in a poor understanding of the controls on biological production in these highly managed river ecosystems. To overcome these limitations, we coupled a two‐station metabolic model of dissolved oxygen dynamics with a hydrologic river routing model. We designed calibration and parameter estimation tools to infer values for hydrologic and metabolic parameters based on time series of water quality data, achieving the ultimate goal of estimating whole‐river gross primary production and ecosystem respiration during dynamic flow conditions. Our case study data for model design and calibration were collected in the tailwater of Glen Canyon Dam (Arizona, U.S.A.), a large hydropower facility where the mean discharge was 325 m3 s−1and the average daily coefficient of variation of flow was 0.17 (i.e., the hydropeaking index averaged from 2006 to 2016). We demonstrate the coupled model's conceptual consistency with conventional models during steady flow conditions, and illustrate the potential bias in metabolism estimates with conventional models during unsteady flow conditions. This effort contributes an approach to solute transport modeling and parameter estimation that allows study of whole‐ecosystem metabolic regimes across a more diverse range of hydrologic conditions commonly encountered in streams and rivers.
A generalized optimization model of microbially driven aquatic biogeochemistry based on thermodynamic, kinetic, and stoichiometric ecological theory
(2014-12) Payn, Robert A.; Helton, A. M.; Poole, Geoffrey C.; Izurieta, Clemente Ignacio; Burgin, A. J.; Bernhardt, E. S.
We have developed a mechanistic model of aquatic microbial metabolism and growth, where we apply fundamental ecological theory to simulate the simultaneous influence of multiple potential metabolic reactions on system biogeochemistry. Software design was based on an anticipated cycle of adaptive hypothesis testing, requiring that the model implementation be highly modular, quickly extensible, and easily coupled with hydrologic models in a shared state space. Model testing scenarios were designed to assess the potential for competition over dissolved organic carbon, oxygen, and inorganic nitrogen in simulated batch reactors. Test results demonstrated that the model appropriately weights metabolic processes according to the amount of chemical energy available in the associated biochemical reactions, and results also demonstrated how simulated carbon, nitrogen, and sulfur dynamics were influenced by simultaneous microbial competition for multiple resources. This effort contributes an approach to generalized modeling of microbial metabolism that will be useful for a theoretically and mechanistically principled approach to biogeochemical analysis.