Area B and Square D.4

Authored section of Heshbon Expedition report

The equations of nature and the nature of equations

Systems of ${N}$ equations in ${N}$ unknowns are ubiquitous in mathematical modeling. These systems, often nonlinear, are used to identify equilibria of dynamical systems in ecology, genomics, control, and many other areas. Structured systems, where the variables that are allowed to appear in each equation are pre-specified, are especially common. For modeling purposes, there is a great interest in determining circumstances under which physical solutions exist, even if the coefficients in the model equations are only approximately known. The structure of a system of equations can be described by a directed graph ${G}$ that reflects the dependence of one variable on another, and we can consider the family ${\mathcal{F}(G)}$ of systems that respect ${G}$. We define a solution ${X}$ of ${F(X) = 0}$ to be robust if for each continuous ${F^*}$ sufficiently close to ${F}$, a solution ${X^*}$ exists. Robust solutions are those that are expected to be found in real systems. There is a useful concept in graph theory called "cycle-coverable". We show that if ${G}$ is cycle-coverable, then for "almost every" ${F\in\mathcal{F}(G)}$ in the sense of prevalence, every solution is robust. Conversely, when ${G}$ fails to be cycle-coverable, each system ${F\in\mathcal{F}(G)}$ has no robust solutions. Failure to be cycle-coverable happens precisely when there is a configuration of nodes that we call a "bottleneck," a criterion that can be verified from the graph. A "bottleneck" is a direct extension of what ecologists call the Competitive Exclusion Principle, but we apply it to all structured systems

Assessing soil quality impacts after conversion of marginal cropland to productive conservation

Planting trees on poor quality cropland may yield some benefits for increasing soil organic carbon. Test plots in four parts of Iowa were examined to determine the level of benefits possible to achieve

Soil properties following reforestation or afforestation of marginal cropland

Aims Reforestation or afforestation of marginal agricultural lands offers opportunities to sequester soil organic carbon (SOC), improve the quality of degraded soils, and provide ecosystem services. The objectives of this study were to identify the extent and distribution of marginally productive cropland in the state of Iowa and to quantify the changes in SOC and relevant soil properties following tree planting. Methods A geographic information system (GIS) analysis was used to identify 1.05 million ha of marginal cropland within the state. Soil samples were collected from four locations with (\u3c51 yr-old) forest plantations and adjacent crop fields. Soil samples were analyzed for SOC, total nitrogen (TN), pH, cation exchange capacity (CEC), ammonium acetateextractable K, Ca, Mg, and Na, and particle size. Results The forested soils had 30.0±5.1 % (mean ±standard error) more SOC than the tilled cropland. The average annual change in SOC following tree planting was estimated to be 0.56±0.05 Mg C ha−1 yr−1. Differences were observed in several soil properties but strong correlations with SOC content were only observed for bulk density and extractable Ca. Conclusions These results indicate that within 5 decades of tree planting on former cropland or pasture there was consistently and significantly greater SOC in soil beneath the trees

Soil properties following reforestation or afforestation of marginal cropland

Aims Reforestation or afforestation of marginal agricultural lands offers opportunities to sequester soil organic carbon (SOC), improve the quality of degraded soils, and provide ecosystem services. The objectives of this study were to identify the extent and distribution of marginally productive cropland in the state of Iowa and to quantify the changes in SOC and relevant soil properties following tree planting. Methods A geographic information system (GIS) analysis was used to identify 1.05 million ha of marginal cropland within the state. Soil samples were collected from four locations with (\u3c51 yr-old) forest plantations and adjacent crop fields. Soil samples were analyzed for SOC, total nitrogen (TN), pH, cation exchange capacity (CEC), ammonium acetateextractable K, Ca, Mg, and Na, and particle size. Results The forested soils had 30.0±5.1 % (mean ±standard error) more SOC than the tilled cropland. The average annual change in SOC following tree planting was estimated to be 0.56±0.05 Mg C ha−1 yr−1. Differences were observed in several soil properties but strong correlations with SOC content were only observed for bulk density and extractable Ca. Conclusions These results indicate that within 5 decades of tree planting on former cropland or pasture there was consistently and significantly greater SOC in soil beneath the trees

Does sexualization in video games cause harm in players? A meta-analytic examination.

Whether video games with sexualized content do or do not relate to mental health and body image problems in players, and/or sexualization and hostility toward women, is an issue of broad public interest. However, evidence from empirical studies has generally been mixed. To examine this issue, we explored the degree to which sexualization in games was related to both well-being/body dissatisfaction and sexism/misogyny among players in two separate meta-analyses. Results revealed that sexualization in games was neither related to well-being/body dissatisfaction (r = 0.082, k = 10, n = 2,010, p = .066) nor sexism/misogyny (r = 0.040, k = 15, n = 15,938, p = .070). Better designed studies, and those that showed less evidence for researcher expectancy effects (for sexism/misogyny outcomes), tended to find less evidence for effects. As appears commonly in other realms of media effects, the evidence is weak that sexualized games influence player attitudes and behavior.</p

Directional is the new null?:A comment on Bushman and Anderson (2021).

Bushman and Anderson (2021) have recently argued that the evidence of harm after playing violent video games is so strong that this effect should be our starting point for future research. They base this claim on an argument that: (a) many professional bodies agree with this opinion; (b) strong theories, such as their General Aggression Model (GAM), predict such outcomes; (c) experimental and meta-analytic studies back such a claim; and (d) people who do not obtain this effect are in the minority and their studies have methodological shortcomings or they misanalyze their data. It is argued here that this is not consistent with the precepts of falsificationism and that: (a) their argument from authority is problematic; (b) they appear unconcerned with, or unaware of, increasing null studies, particularly missing out international research or preregistered studies; (c) the majority of research groups outside of Bushman/Anderson and their coauthors do not concur with their results; and (d) there are theories (e.g., the Immersive Media Prediction model) which better account for the data than their GAM. It is also argued that when theories and data collide it is the theories that need revision not the data, demand effects of researchers are strong in gaming research, and Bushman and Anderson’s results may themselves have been influenced by their methodology rather than accurately describing a naturally occurring weakness in the human condition. It is also argued that we need a more collegial approach to gaming research and make suggestions to facilitate this shift

The effects of non-diagnostic information on confidence and decision making

Many decision-making tasks are characterized by a combination of diagnostic and non-diagnostic information, yet models of responding and confidence almost exclusively focus on the contribution of diagnostic information (e.g., evidence associated with stimulus discriminability), largely ignoring the contribution of non-diagnostic information. An exception is Baranski and Petrusic's Journal of Experimental Psychology: Human Perception and Performance, 24(3), 929-945, (1998) doubt-scaling model, which predicts a negative relationship between non-diagnostic information and confidence, and between non-diagnostic information and accuracy. In two perceptual-choice tasks, we tested the effects of manipulating non-diagnostic information on confidence, accuracy and response time (RT). In Experiment 1, participants viewed a dynamic grid consisting of flashing blue, orange and white pixels and indicated whether the stimulus was predominantly blue or orange (using a response scale ranging from low-confidence blue to high-confidence orange), with the white pixels constituting non-diagnostic information. Increasing non-diagnostic information reduced both confidence and accuracy, generally slowed RTs, and led to an increase in the speed of errors. Experiment 2 replicated these results for a decision-only task, providing further support for the doubt-scaling model of confidence.</p