ASU Scholarship Showcase

In this synthesis, we hope to accomplish two things: 1) reflect on how the analysis of the new archaeological cases presented in this special feature adds to previous case studies by revisiting a set of propositions reported in a 2006 special feature, and 2) reflect on four main ideas that are more specific to the archaeological cases: i) societal choices are influenced by robustness–vulnerability trade-offs, ii) there is interplay between robustness–vulnerability trade-offs and robustness–performance trade-offs, iii) societies often get locked in to particular strategies, and iv) multiple positive feedbacks escalate the perceived cost of societal change. We then discuss whether these lock-in traps can be prevented or whether the risks associated with them can be mitigated. We conclude by highlighting how these long-term historical studies can help us to understand current society, societal practices, and the nexus between ecology and society.

Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

The objective of articulating sustainability visions through modeling is to enhance the outcomes and process of visioning in order to successfully move the system toward a desired state. Models emphasize approaches to develop visions that are viable and resilient and are crafted to adhere to sustainability principles. This approach is largely assembled from visioning processes (resulting in descriptions of desirable future states generated from stakeholder values and preferences) and participatory modeling processes (resulting in systems-based representations of future states co-produced by experts and stakeholders). Vision modeling is distinct from normative scenarios and backcasting processes in that the structure and function of the future desirable state is explicitly articulated as a systems model. Crafting, representing and evaluating the future desirable state as a systems model in participatory settings is intended to support compliance with sustainability visioning quality criteria (visionary, sustainable, systemic, coherent, plausible, tangible, relevant, nuanced, motivational and shared) in order to develop rigorous and operationalizable visions. We provide two empirical examples to demonstrate the incorporation of vision modeling in research practice and education settings. In both settings, vision modeling was used to develop, represent, simulate and evaluate future desirable states. This allowed participants to better identify, explore and scrutinize sustainability solutions.

It has become common for sustainability science and resilience theory to be considered as complementary approaches. Occasionally the terms have been used interchangeably. Although these two approaches share some working principles and objectives, they also are based on some distinct assumptions about the operation of systems and how we can best guide these systems into the future. Each approach would benefit from some scholars keeping sustainability science and resilience theory separate and focusing on further developing their distinctiveness and other scholars continuing to explore them in combination. Three areas of research in which following different procedures might be beneficial are whether to prioritize outcomes or system dynamics, how best to take advantage of community input, and increasing the use of knowledge of the past as a laboratory for potential innovations.

The context in which many self-governed commons systems operate will likely be significantly altered as globalization processes play out over the next few decades. Such dramatic changes will induce some systems to fail and subsequently to be transformed, rather than merely adapt. Despite this possibility, research on globalization-induced transformations of social-ecological systems (SESs) is still underdeveloped. We seek to help fill this gap by exploring some patterns of transformation in SESs and the question of what factors help explain the persistence of cooperation in the use of common-pool resources through transformative change. Through the analysis of 89 forest commons in South Korea that experienced such transformations, we found that there are two broad types of transformation, cooperative and noncooperative. We also found that two system-level properties, transaction costs associated group size and network diversity, may affect the direction of transformation. SESs with smaller group sizes and higher network diversity may better organize cooperative transformations when the existing system becomes untenable.

The effects of urbanization on ozone levels have been widely investigated over cities primarily located in temperate and/or humid regions. In this study, nested WRF-Chem simulations with a finest grid resolution of 1 km are conducted to investigate ozone concentrations O₃ due to urbanization within cities in arid/semi-arid environments. First, a method based on a shape preserving Monotonic Cubic Interpolation (MCI) is developed and used to downscale anthropogenic emissions from the 4 km resolution 2005 National Emissions Inventory (NEI05) to the finest model resolution of 1 km. Using the rapidly expanding Phoenix metropolitan region as the area of focus, we demonstrate the proposed MCI method achieves ozone simulation results with appreciably improved correspondence to observations relative to the default interpolation method of the WRF-Chem system. Next, two additional sets of experiments are conducted, with the recommended MCI approach, to examine impacts of urbanization on ozone production: (1) the urban land cover is included (i.e., urbanization experiments) and, (2) the urban land cover is replaced with the region's native shrubland. Impacts due to the presence of the built environment on O₃ are highly heterogeneous across the metropolitan area. Increased near surface O₃ due to urbanization of 10–20 ppb is predominantly a nighttime phenomenon while simulated impacts during daytime are negligible. Urbanization narrows the daily O₃ range (by virtue of increasing nighttime minima), an impact largely due to the region's urban heat island. Our results demonstrate the importance of the MCI method for accurate representation of the diurnal profile of ozone, and highlight its utility for high-resolution air quality simulations for urban areas.

Introduction: Urbanization can considerably impact animal ecology, evolution, and behavior. Among the new conditions that animals experience in cities is anthropogenic noise, which can limit the sound space available for animals to communicate using acoustic signals. Some urban bird species increase their song frequencies so that they can be heard above low-frequency background city noise. However, the ability to make such song modifications may be constrained by several morphological factors, including bill gape, size, and shape, thereby limiting the degree to which certain species can vocally adapt to urban settings. We examined the relationship between song characteristics and bill morphology in a species (the house finch, Haemorhous mexicanus) where both vocal performance and bill size are known to differ between city and rural animals.

Results: We found that bills were longer and narrower in more disturbed, urban areas. We observed an increase in minimum song frequency of urban birds, and we also found that the upper frequency limit of songs decreased in direct relation to bill morphology.

Conclusions: These findings are consistent with the hypothesis that birds with longer beaks and therefore longer vocal tracts sing songs with lower maximum frequencies because longer tubes have lower-frequency resonances. Thus, for the first time, we reveal dual constraints (one biotic, one abiotic) on the song frequency range of urban animals. Urban foraging pressures may additionally interact with the acoustic environment to shape bill traits and vocal performance.

There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km[superscript 2] land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.

As part of an international collaboration to compare large-scale commons, we used the Social-Ecological Systems Meta-Analysis Database (SESMAD) to systematically map out attributes of and changes in the Great Barrier Reef Marine Park (GBRMP) in Australia. We focus on eight design principles from common-pool resource (CPR) theory and other key social-ecological systems governance variables, and explore to what extent they help explain the social and ecological outcomes of park management through time. Our analysis showed that commercial fisheries management and the re-zoning of the GBRMP in 2004 led to improvements in ecological condition of the reef, particularly fisheries. These boundary and rights changes were supported by effective monitoring, sanctioning and conflict resolution. Moderate biophysical connectivity was also important for improved outcomes. However, our analysis also highlighted that continued challenges to improved ecological health in terms of coral cover and biodiversity can be explained by fuzzy boundaries between land and sea, and the significance of external drivers to even large-scale social-ecological systems (SES). While ecological and institutional fit in the marine SES was high, this was not the case when considering the coastal SES. Nested governance arrangements become even more important at this larger scale. To our knowledge, our paper provides the first analysis linking the re-zoning of the GBRMP to CPR and SES theory. We discuss important challenges to coding large-scale systems for meta-analysis.