Austin Peay State University

Abstract Organisms that move across ecosystem boundaries can create resource gradients in adjacent ecosystems. Midges that emerge from Lake Mývatn, Iceland, are deposited as nutrient-rich carcasses on land, and this generates a nutrient gradient from its shoreline. We contrasted the distribution and photobiont characteristics of two lichen species, Nephromopsis cucullata and Peltigera leucophlebia, in a lava field adjacent to Mývatn along this midge deposition gradient. While the primary photobionts of P . leucophlebia and N . cucullata are green algae, P . leucophlebia also contain nitrogen-fixing cyanobacteria. Because the landscape surrounding Mývatn is generally nitrogen-limited and midge carcasses are nitrogen-rich, we hypothesized that the different photobiont associations would result in contrasting distributions between species and contrasting photobiont characteristics within species along the midge gradient. We found that N . cucullata was at its highest density 100 m from the lakeshore, while P . leucophlebia was more evenly distributed. Both species had a larger photobiont layer thickness near the lakeshore, although N . cucullata had a negative relationship between chlorophyll-a concentration (as measured using a greenness index) and distance, whereas P . leucophlebia showed no pattern. The contrasts between N . cucullata and P . leucophlebia may be related to the presence of cyanobacteria in P . leucophlebia , which provide nitrogen and, in part, decouple P . leucophlebia from the midge nitrogen deposition gradient. This study illustrates that nutrient gradients may alter lichen distributions and photobiont characteristics and suggests that these responses are primarily taxon-specific. Furthermore, it highlights the capacity for the dynamics of aquatic ecosystems to propagate into adjacent landscapes.

Abstract Inorganic nutrients can vary over space and time in lakes due to abiotic and biotic factors. To understand the drivers of spatial variation in nutrients in shallow Lake Mývatn, Iceland, we mapped dissolved inorganic and organic nitrogen (N) and phosphorus (P) concentrations in benthic (interstitial) and pelagic zones in summer 2021 during a lake-wide pelagic cyanobacterial bloom and in spring 2022 when there was no cyanobacterial bloom. We expected the bloom to affect nutrient distributions between zones through shading and N 2 fixation. Nutrient concentrations were neither correlated between the benthic and pelagic zone nor correlated between sampling events. The low ratio of total dissolved N to P implied that the benthos was N-limited, while the pelagic zone switched from N to P limitation during the summer cyanobacterial bloom. Interstitial ammonium concentrations were higher in summer than spring sampling events, positively associated with depth, and negatively associated with epipelic diatom density, associations that were stronger in spring. In summer, pelagic phosphate was highest near the springs; this pattern was reversed in the spring sampling event, although the effect size was weak. Variation in interstitial phosphate was not strongly associated with any of our predictors. Pelagic ammonium, and both interstitial and pelagic nitrate, were near or below detection limits. Together, these results highlight the capacity for large spatiotemporal variation in dissolved inorganic and organic N and P concentrations and nutrient limitation which will impact the structure and function of the Mývatn ecosystem.

Abstract In nitrogen-limited lakes, cyanobacterial nitrogen (N) fixation rates can be high. However, the effect of cyanobacterial N fixation on ecosystem N availability depends, in part, on the retention of fixed N. Here, we explore patterns of benthic and pelagic N fixation and fixed N retention in Lake Mývatn, Iceland. Our N budget, updated from previous assessments, showed that the annual rate of lake-scale N fixation needed to account for the deficit between external N supply and N loss varies greatly from year to year, primarily due to variation in the magnitude of N loss via outflow and midge emergences that transport and deposit N on the shore. Extrapolations from recent direct measurements of pelagic and benthic N fixation under scenarios of different benthic and pelagic N-fixer abundances showed that the capacity for N fixation is much higher in the pelagic than the benthic zone. We then explored the degree to which N fixed in the pelagic zone is retained and recycled within the lake using a dynamic N budget that leverages data on patterns of N loss via the outlet river Laxá. This revealed that approximately two-thirds of organic N in the pelagic zone must be incorporated into the benthos to explain patterns of N loss via outflow from the river Laxá. Together, these results suggest N fixation in pelagic cyanobacterial blooms may supply most of the N for benthic primary production in Mývatn.

ABSTRACT This paper examines the impact of the North American Free Trade Agreement (NAFTA) on U.S. housing markets by exploiting regional variation in exposure to Mexican import competition. Using a newly constructed vulnerability index based on pre‐NAFTA industrial composition and Mexico's revealed comparative advantage, we implement an event‐study and difference‐in‐differences specifications across U.S. Public Use Microdata Areas (PUMAs) from 1989 to 2007. We find that regions more exposed to NAFTA‐induced import competition experienced significant and persistent declines in housing prices. Specifically, a one‐unit increase in trade vulnerability, corresponding to roughly a two‐standard‐deviation increase in exposure, led to a 6% decline in the housing price index and a $17.5K reduction in average house value (approximately 10% of the mean). In exploring potential mechanisms, we find reductions of approximately 5%–8% in income and wages, suggesting that weakened demand contributed to the downward pressure on housing prices. These effects were particularly concentrated in the manufacturing sector. Our findings highlight housing markets as an important, yet understudied, channel through which trade liberalization affects local economies.

This essay reexamines Elizabeth Bishop’s use of simile, challenging critical accounts that, since antiquity, have dismissed the device as limited in ambition or overly literal. Contrary to the claim that simile is a “half-measure trope” (per Bonnie Costello), solely limited to “local degrees of likeness” (per Denis Donoghue), Bishop’s poetry demonstrates a capacity to exceed and transform reality, as well as to create multiple possible interpretations. Indeed, Bishop’s similes are ambitious: they do not merely reflect reality but seek to transform it within the poem, by reshaping the poet’s and reader’s perception of their subject. They also foster multiplicity and ambiguity over precision and clarity—the two traits most often ascribed to Bishop’s style. It is typically said that Bishop uses simile to contain emotions, particularly those drawn from her life, by confining them to a single image. In fact, her similes generate several competing interpretations that cannot easily be resolved through an autobiographical reading. In sum, Bishop’s use of simile offers a new look at this poetic device, demonstrating its ability to disrupt and expand a poem’s meaning.

ABSTRACT Preservation of community safety is inextricably tied to police personnel wellbeing. When considering that an alarming number of police personnel are experiencing serious health impairments, a pressing need emerges to address this issue. In response, the current study conducted semi-structured interviews with (n = 18) sworn and (n = 8) civilian police personnel from a Mid-Atlantic police department to acquire insight into the health services they need. Thematic analysis of interview data produced several key themes related to police health. These included considerations for: 1) improved physical fitness, 2) improved nutrition and wellness, 3) the construction of a built environment inside police agencies to promote police wellbeing, 4) active agency assistance, 5) peer support, and 6) the integration of clinical therapy into police departments. Findings contribute to the development of necessary health interventions that can optimize police personnel’s ability to fulfill their important service-provider functions.

Healthcare fraud and abuse remain critical challenges that undermine financial integrity and service delivery within health systems. This study developed a national predictive analytics framework to identify and prevent healthcare fraud using anomaly detection techniques. The analysis employed machine learning algorithms to evaluate claim patterns across different payer types and geographic regions. Results revealed that most healthcare claims exhibited normal behavioral patterns, while a small subset demonstrated significantly high anomaly scores, suggesting potential fraudulent activities. Commercial payers recorded the highest proportion of anomalies, followed by Medi-Cal, whereas Medicare showed the lowest frequency of irregular claims. Geographic analysis indicated that Los Angeles had the greatest concentration of anomalous records, highlighting a strong spatial clustering effect in high-volume urban regions. The correlation between Z-scores and anomaly scores confirmed the reliability of the model in detecting statistical deviations and behavioral inconsistencies. These findings emphasize the importance of integrating predictive analytics into healthcare oversight mechanisms to improve transparency, accountability, and operational efficiency. The study concludes that adopting data-driven fraud detection systems can significantly strengthen institutional capacity for proactive fraud prevention. Furthermore, the framework provides a scalable model that regulatory agencies and health insurers can adapt to monitor claim integrity, allocate resources efficiently, and sustain trust within the healthcare ecosystem.

The convenience of digital banking services has transformed the global financial industry and is now available to consumers all over the world. As with any advancement, there’s an increase in associated risk. In this case, we have an upsurge in fraudulent activity, the mobility of cybercriminals, and their more advanced technologies to breach vulnerabilities within digital infrastructures. Indeed, financial crimes are constantly evolving like the rest of technology and society. Those who monitor and manually analyse systems are no match for the speed at which criminals can devise new rule-of-thumb schemes. This article examines how artificial intelligence and machine learning can reform the detection of fraud within digital banking systems. The research analyses different techniques of AI and ML, supervised learning, unsupervised learning, ensemble, and deep learning approaches, while also observing their uses in practical fraud detection systems. The paper also analyses the ethical and legal concerns involving the use of AI within banking, considering data issues, algorithmic discrimination, and other contentious aspects of legal compliance, including quasi-legal frameworks like GDPR and PCI-DSS. It also explores some of the newer directions in AI, like quantum computing, explainable AI (XAI), and federated learning, and their potential implications to improving fraud detection systems performance. Finally, the focus of this paper has been on a continuing effort and partnership across sectors in building resilient, secure, transparent financial systems. AI, ML, and blockchain technologies enhance the capability to prevent fraud in digital banking, while ensuring and maintaining customer trust and security in financial transactions.

Abstract Despite the well-established links between coparenting and child behavior problems, little is known about the longitudinal development of dyadic mothers’ and fathers’ coparenting relationships within the larger family context. Informed by family systems theory, this study sought to examine how maternal and paternal coparenting systems were simultaneously manifested over time from infancy (Year 1) to preadolescence (Year 9), and how various antecedents at age 1 were associated with the patterns of dyadic longitudinal coparenting change. Further, we tested whether distinct coparenting patterns were related to later adolescent behavior problems (Year 15) even after accounting for early behavior problems, child gender, and demographic risk factors. This study leveraged the data of the Future of Families and Child Wellbeing Study ( N = 3923 families), which oversampled unmarried, low-income, and ethnically and racially diverse families. Growth mixture models identified four trajectory change patterns of mothers’ and fathers’ coparenting (latent classes): (a) both high stable support (69.7%); (b) father less-supportive (15.4%); (c) both decreasing support (8.1%); and (d) father unsupportive (6.8%) coparenting classes. Further analyses revealed that adolescents in the both high stable support coparenting class showed significantly fewer internalizing and externalizing behavior problems at age 15, compared to other classes. Findings highlight dyadic coparenting processes in family systems, which may help inform development of targeted programs to promote adaptive coparenting in mothers and fathers among ethnically, racially, and economically diverse families.