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Introduction: Large-scale investments in restoring California’s forested watersheds are imperative for conserving biodiversity, enhancing water quality, and mitigating the future impacts of climate change. This study explores the underlying incentives, major challenges, and potential strategies associated with such investments. Methods: An online survey was administered to 43 experts in the field to gather their insights on forest watershed restoration investments. The collected responses were then analyzed using a combination of confirmatory factor analysis and regression analysis to elucidate patterns and relationships. Results: The analysis revealed that key environmental outcomes, such as reducing wildfire risks and protecting water supplies, are the principal motivators driving investment. At the same time, significant barriers emerged, including high costs, limited workforce capacity, and insufficient trust among stakeholders. The study also identified a series of effective strategies to overcome these obstacles, such as repositioning forest restoration as an infrastructure investment and clearly demonstrating its ecological, social, and economic benefits. Discussion: Overall, the findings underscore the need for more flexible funding frameworks, enhanced stakeholder engagement, and improved data infrastructures. By addressing these elements, policymakers and practitioners can pave the way for more resilient and sustainable forested-watershed ecosystems in California. 

Abstract Data visualizations play a crucial role in communicating patterns in quantitative data, making data visualization literacy a key target of STEM education. However, it is currently unclear to what degree different assessments of data visualization literacy measure the same underlying constructs. Here, we administered two widely used graph comprehension assessments (Galesic and Garcia-Retamero in Med Dec Mak 31:444–457, 2011; Lee et al. in IEEE Trans Vis Comput Graph 235:51–560, 2016) to both a university-based convenience sample and a demographically representative sample of adult participants in the USA (N=1,113). Our analysis of individual variability in test performance suggests that overall scores are correlated between assessments and associated with the amount of prior coursework in mathematics. However, further exploration of individual error patterns suggests that these assessments probe somewhat distinct components of data visualization literacy, and we do not find evidence that these components correspond to the categories that guided the design of either test (e.g., questions that require retrieving values rather than making comparisons). Together, these findings suggest opportunities for development of more comprehensive assessments of data visualization literacy that are organized by components that better account for detailed behavioral patterns. 

Small multiples are a popular visualization method, displaying different views of a dataset using multiple frames, often with the same scale and axes. However, there is a need to address their potential constraints, especially in the context of human cognitive capacity limits. These limits dictate the maximum information our mind can process at once. We explore the issue of capacity limitation by testing competing theories that describe how the number of frames shown in a display, the scale of the frames, and time constraints impact user performance with small multiples of line charts in an energy grid scenario. In two online studies (Experiment 1 n = 141 and Experiment 2 n = 360) and a follow-up eye-tracking analysis (n = 5), we found a linear decline in accuracy with increasing frames across seven tasks, which was not fully explained by differences in frame size, suggesting visual search challenges. Moreover, the studies demonstrate that highlighting specific frames can mitigate some visual search difficulties but, surprisingly, not eliminate them. This research offers insights into optimizing the utility of small multiples by aligning them with human limitations. 

Many papers make claims about specific visualization techniques that are said to enhance or calibrate trust in AI systems. But a design choice that enhances trust in some cases appears to damage it in others. In this paper, we explore this inherent duality through an analogy with “knobs”. Turning a knob too far in one direction may result in under-trust, too far in the other, over-trust or, turned up further still, in a confusing distortion. While the designs or so-called “knobs” are not inherently evil, they can be misused or used in an adversarial context and thereby manipulated to mislead users or promote unwarranted levels of trust in AI systems. When a visualization that has no meaningful connection with the underlying model or data is employed to enhance trust, we refer to the result as “trust junk.” From a review of 65 papers, we identify nine commonly made claims about trust calibration. We synthesize them into a framework of knobs that can be used for good or “evil,” and distill our findings into observed pitfalls for the responsible design of human-AI systems. 

The social impacts of natural resource management are challenging to evaluate because their perceived benefits and costs vary across stakeholder groups. Nevertheless, ensuring social acceptance is essential to building public support for adaptive measures required for the sustainable management of ecosystems in a warming climate. Based on surveys with both members of the public and natural-resource professionals in California, we applied structural-equation modeling to examine how psychological factors impact individuals' attitudes toward management's capacity to reduce the impacts of disturbance events, including wildfires, smoke from wildfires, drought, water shortages, tree mortality, and utility failure. We found the members of the public more optimistic than natural-resource professionals, perceiving management capacity to be on average 3.04 points higher (of 10) and displaying higher levels of trust of the government on both the state (Δ = 11%) and federal levels (Δ = 19%). Personal experience with natural-resource events had a positive effect on perceived management in both the public (1.26) and the professional samples (5.05), whereas perceived future risk had a negative effect within both samples (professional = −0.91, public = −0.45). In addition, higher trust and perceived management effectiveness were also linked with higher perceptions of management capacity in the public sample (1.81 versus 1.24), which could affect the acceptance of management actions. Continued social acceptance in a period of increasing risk may depend on managers sharing personal experiences and risk perception when communicating with the public. The contemporary shift toward multibenefit aims is an important part of that message.