Gigi Owen

Bio: Gigi Owen is an academic researcher from University of Arizona. The author has contributed to research in topics: Vulnerability & Stakeholder engagement. The author has an hindex of 8, co-authored 16 publications receiving 702 citations.

Moving toward the Deliberate Coproduction of Climate Science Knowledge

Abstract: Coproduction of knowledge is believed to be an effective way to produce usable climate science knowledge through a process of collaboration between scientists and decision makers. While the general principles of coproduction—establishing long-term relationships between scientists and stakeholders, ensuring two-way communication between both groups, and keeping the focus on the production of usable science—are well understood, the mechanisms for achieving those goals have been discussed less. It is proposed here that a more deliberate approach to building the relationships and communication channels between scientists and stakeholders will yield better outcomes. The authors present five approaches to collaborative research that can be used to structure a coproduction process that each suit different types of research or management questions, decision-making contexts, and resources and skills available to contribute to the process of engagement. By using established collaborative research approaches scientists can be more effective in learning from stakeholders, can be more confident when engaging with stakeholders because there are guideposts to follow, and can assess both the process and outcomes of collaborative projects, which will help the whole community of stakeholder-engaged climate-scientists learn about coproduction of knowledge.

What makes climate change adaptation effective? A systematic review of the literature

Abstract: Increased understanding of global warming and documentation of its observable impacts have led to the development of adaptation responses to climate change around the world. A necessary, but often missing, component of adaptation involves the assessment of outcomes and impact. Through a systematic review of research literature, I categorize 110 adaptation initiatives that have been implemented and shown some degree of effectiveness. I analyze the ways in which these activities have been documented as effective using five indicators: reducing risk and vulnerability, developing resilient social systems, improving the environment, increasing economic resources, and enhancing governance and institutions. The act of cataloging adaptation activities produces insights for current and future climate action in two main areas: understanding common attributes of adaptation initiatives reported to be effective in current literature; and identifying gaps in adaptation research and practice that address equality, justice, and power dynamics.

Wildfire Management and Forecasting Fire Potential: The Roles of Climate Information and Social Networks in the Southwest United States

Abstract: Continuing progress in the fields of meteorology, climatology, and fire ecology has enabled more proactive and risk-tolerant wildland fire management practices in the United States. Recent institutional changes have also facilitated the incorporation of more advanced climate and weather research into wildland fire management. One of the most significant changes was the creation of Predictive Services in 1998, a federal interagency group composed, in part, of meteorologists who create climate- and weather-based fire outlooks tailored to fire manager needs. Despite the numerous forecast products now available to fire managers, few studies have examined how these products have affected their practices. In this paper the authors assess how fire managers in the Southwest region of the United States perceive and incorporate different types of information into their management practices. A social network analysis demonstrates that meteorologists have become central figures in disseminating information in...

Community and Ecosystem Effects of Buffelgrass (Pennisetum ciliare) and Nitrogen Deposition in the Sonoran Desert

Abstract: Buffelgrass is a non-indigenous, invasive, C4 grass that was introduced throughout much of southern Texas, the Southwestern United States, and northern and central Mexico to improve degraded rangelands. The successful introduction and spread of buffelgrass follows a trajectory similar to that of other invasive C4 grasses in arid and semiarid ecosystems. In the Plains of Sonora of the Sonoran Desert (Mexico) buffelgrass is favored by widespread removal of native vegetation and seeding, but, why, following initial introduction, the species persists remains unclear. In this study, we addressed two concerns associated with buffelgrass invasion in the Plains of Sonora. We hypothesized that under arid rangeland conditions, buffelgrass outcompetes native herbaceous species (1) through rapid acquisition of limiting nutrients (here assumed to be nitrogen) and (2) under conditions with high nitrogen input. In summer 2002, a 2 by 2 factorial experiment was established with buffelgrass removal and nitrogen a...

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Models and methods in social network analysis

Abstract: Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

Crafting usable knowledge for sustainable development

Abstract: This paper distills core lessons about how researchers (scientists, engineers, planners, etc.) interested in promoting sustainable development can increase the likelihood of producing usable knowledge. We draw the lessons from both practical experience in diverse contexts around the world and from scholarly advances in understanding the relationships between science and society. Many of these lessons will be familiar to those with experience in crafting knowledge to support action for sustainable development. However, few are included in the formal training of researchers. As a result, when scientists and engineers first venture out of the laboratory or library with the goal of linking their knowledge with action, the outcome has often been ineffectiveness and disillusionment. We therefore articulate here a core set of lessons that we believe should become part of the basic training for researchers interested in crafting usable knowledge for sustainable development. These lessons entail at least four things researchers should know, and four things they should do. The knowing lessons involve understanding the coproduction relationships through which knowledge making and decision making shape one another in social–environmental systems. We highlight the lessons that emerge from examining those coproduction relationships through the ICAP lens, viewing them from the perspectives of Innovation systems, Complex systems, Adaptive systems, and Political systems. The doing lessons involve improving the capacity of the research community to put its understanding of coproduction into practice. We highlight steps through which researchers can help build capacities for stakeholder collaboration, social learning, knowledge governance, and researcher training.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Abstract: Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure Effective forecasting will also require changes in scientific training, culture, and institutions The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward