An Experimental Analysis of Nash Refinements in Signaling Games

This paper investigates the refinements of Nash equilibrium in two person signaling game experiments. The experimental games cover the watershed of the nested refinements: Bayes-Nash, Sequential, Intuitive, Divine, Universally Divine, NWBR, and Stabel. In each game an equilibrium selection problem is defined in which adjacent refinements are considered. The pattern of outcomes suggest that individuals select the more refined equilibria up to the divinity concept. However, an anomaly occurs in the game in which the stable equilibrium is a clear preference among the subjects. Since the concepts are nested this suggests that the outcomes are game specific. Sender behavior does not seem to follow any specific decision rule (e.g., Nash, minmax, PIR, etc.) while receiver actions tend to correspond to the Nash equilibrium outcomes

Underreporting of wildlife-vehicle collisions does not hinder predictive models for large ungulates

Conflicts from wildlife–vehicle collisions (WVCs) pose serious challenges for managing and conserving large ungulates throughout the world. However, underreporting of large proportions of WVCs (i.e., two-thirds of WVCs in some cases) creates concern for relying on governmental databases to inform management strategies of WVCs. Our objective was to test the sensitivity of WVC studies to underreporting using 2 species of large ungulates that experience substantial incidences of collisions but exist in different environmental settings: white-tailed deer (Odocoileus virginianus) in agricultural-dominated central Illinois and moose (Alces alces) in forest-dominated western Maine, USA. We estimated baseline relationships between the landscape, traffic, and abundance of wildlife on the probabilities of WVCs using the total number of reported WVCs. Then, we simulated underreporting by randomly excluding reports of WVCs and evaluated for relative changes in precision, parameter estimates, and prediction. Point estimates of the relationships between environmental influences and WVCs for both species were reliable until high rates of underreporting occurred (≥70%). When underreporting occurred with spatial bias, shifts in point estimates were detected only for variables that spatially-corresponded with the rate of reporting. Prediction estimates for both species were also reliable until high rates of underreporting occurred (≥75%). These findings suggest that predictive models generate reliable estimates about WVCs with large ungulates unless underreporting is severe; possibly because they occur in non-random patterns (i.e., hotspots) and variability in their environment influences is low. We recommend that concern about underreporting not impede research with existing databases, such as those in this study, for analyzing predictive models and developing management strategies for reducing WVCs

A landscape-based approach for delineating hotspots of wildlife-vehicle collisions

Imposing human perceptions about the scales of ecological processes can produce unreliable scientific inferences in wildlife research and possibly misinform mitigation strategies. An example of this disconnect occurs in studies of wildlife-vehicle collisions (WVCs). Subjective procedures are often used to delineate hotspots of WVCs, resulting in hotspots that are not spatially independent. We developed a new approach that identifies independent hotspots using attributes of the landscape to inform delineations instead of subjective measures. First, we generated a candidate set of grouping scenarios using unique combinations of kernel-density estimation parameterization (i.e., bandwidth and isopleth values). Next, we associated the groups of WVCs with attributes of the surrounding landscape. Finally, we identified the grouping scenario with the highest amount of variation in the landscape among the groups. The highest variation corresponded to hotspots that were most distinguishable from each other (i.e., most independent) based on the surrounding landscape. We tested our approach on 3 species of wildlife [island foxes (Urocyon littoralis) on San Clemente Island, CA; white-tailed deer (Odocoileus virginianus) in Onondaga County, NY; and moose (Alces alces) in western Maine] that exemplified varying degrees of space-use in different landscapes. We found that the landscape based approach was able to effectively delineate independent hotspots for each species without using subjective measures. The landscape-based approach delineated fewer or larger hotspots than currently used methods, suggesting a reduction in spatial dependency among hotspots. Variation in the landscape indicated that hotspots may be larger than previously identified; therefore current mitigation strategies should be adjusted to include larger areas of high risk

A landscape-based approach for delineating hotspots of wildlife-vehicle collisions

Imposing human perceptions about the scales of ecological processes can produce unreliable scientific inferences in wildlife research and possibly misinform mitigation strategies. An example of this disconnect occurs in studies of wildlife-vehicle collisions (WVCs). Subjective procedures are often used to delineate hotspots of WVCs, resulting in hotspots that are not spatially independent. We developed a new approach that identifies independent hotspots using attributes of the landscape to inform delineations instead of subjective measures. First, we generated a candidate set of grouping scenarios using unique combinations of kernel-density estimation parameterization (i.e., bandwidth and isopleth values). Next, we associated the groups of WVCs with attributes of the surrounding landscape. Finally, we identified the grouping scenario with the highest amount of variation in the landscape among the groups. The highest variation corresponded to hotspots that were most distinguishable from each other (i.e., most independent) based on the surrounding landscape. We tested our approach on 3 species of wildlife [island foxes (Urocyon littoralis) on San Clemente Island, CA; white-tailed deer (Odocoileus virginianus) in Onondaga County, NY; and moose (Alces alces) in western Maine] that exemplified varying degrees of space-use in different landscapes. We found that the landscape based approach was able to effectively delineate independent hotspots for each species without using subjective measures. The landscape-based approach delineated fewer or larger hotspots than currently used methods, suggesting a reduction in spatial dependency among hotspots. Variation in the landscape indicated that hotspots may be larger than previously identified; therefore current mitigation strategies should be adjusted to include larger areas of high risk

Developing a Glacial Surface Model for Greenland to Improve the Projections of Surface Runoff

Over the past several decades, the Greenland Ice Sheet has been losing mass through a combination of increased surface runoff and accelerating ice flux to the ocean. Our understanding of the surface component is drawn heavily from satellite observations and climate models. The MAR (Modèle Atmosphère Régional) model is a 3D regional climate model used extensively in Greenland because of its proven record at simulating precipitation and firn and snowpack evolution over glaciated surfaces. Our study focuses on the surface snow and the ice down to 15-meter in depth. A light-weighted surface model for us to integrate the local observation data and force many simulations is needed. Our goal is to develop a surface-only model, derived from MAR, as a tool for understanding the glacial surface components, correlations, and MAR biases to improve projections of surface runoff. This model includes the ability to integrate observations from surface weather stations, translate the data into a model forcing format, force different simulations with various configurations or datasets, visualize model outputs, find key correlations between atmospheric drivers and modeled firn desertification. In the model development, we extract the surface code from the full MAR for the simulations initialized and forced with the following snow and atmospheric fields: snow depth, temperature, density, water volume, and grain size. We then verify that the surface model generates the same outputs as the full MAR does if fetched with the identical data. The bias is checked with snowpack time-depth plots for multiple sites around Greenland, including Summit and Swiss Camp. We have found a very small bias when compared to the fully-coupled MAR. We perform quality control for the data inputs, such as replacing missing data from the station measurements, defining the max and min for each dataset, filtering out the data outliers by statistics standard deviations. As the result, our model software can provide multiple simulations in sequential and concurrent mode with user-friendly interfaces, and run robustly. The model’s first release is currently being deployed over different sites across Greenland to understand the importance of atmospheric forcing versus snow model biases in projections of future mass loss due to surface melt

Photoperiodic effects on precocious maturation, growth and smoltification in Atlantic salmon, Salmo salar

Current Atlantic salmon farming practice induces early smoltification with artificial photoperiod regimes, however the importance of these photoperiods on parr maturation and interactions with smoltification are poorly understood. These questions were addressed in the present investigation, which examined the effects of photoperiod manipulation on the development, maturation and smoltification of individually tagged parr. Approximately 9000 salmon parr from a high grilsing stock were exposed to continuous light (LL) from first feeding. Three sub-groups of 2400 parr, each sub-group in triplicate tanks, were then exposed to an 8 week “winter photoperiod” (LD 10:14) starting on either the 18th May, the 9th August or the 20th September (defined respectively as the May, August and September groups). Following the artificial winter each group was returned to LL. A fourth group of 1600 fish was maintained in replicate tanks on LL throughout. The highest levels of maturation (approx. 20%) were recorded in the May group. August and September groups showed low levels of maturity

Identifying Spatial Variability in Greenland’s Outlet Glacier Response to Ocean Heat

Although the Greenland ice sheet is losing mass as a whole, patterns of change on both local and regional scales are complex. Spatial statistics reveal large spatial variability of dynamic thinning rates of Greenland’s marine-terminating glaciers between 2003 and 2009; only 18% of glacier thinning rates co-vary with neighboring glaciers. Most spatially-correlated thinning rates are clusters of stable glaciers in the Thule, Scoresby Sund, and Southwest regions. Conversely, where spatial-autocorrelation is low, individual glaciers are more strongly controlled by local, glacier-scale features than by regional influences. We investigate possible sources of local control of oceanic forcing by combining grounding line depths and ocean model output to estimate mean ocean heat content adjacent to 74 glaciers. Linear regression models indicate stronger correlation of dynamic thinning rates with ocean heat content compared to those with grounding line depths alone. The correlation between ocean heat and dynamic thinning is robust for all of Greenland except glaciers in the West, and strongest in the Southeast (R2 ∼ 0.81 ± 0.15, ρ = 0.009), implying that glaciers with deeper grounded termini here are most sensitive to changes in ocean forcing. In the Northwest, accounting for shallow sills in the regressions improves the correlation of water depth with glacial thinning, highlighting the need for comprehensive knowledge of fjord geometry

Notes on the Music: A social data infrastructure for music annotation

Beside transmitting musical meaning from composer to reader, symbolic music notation affords the dynamic addition of layers of information by annotation. This allows music scores to serve as rudimentary communication frameworks. Music encodings bring these affordances into the digital realm; though annotations may be represented as digital pen-strokes upon a score image, they must be captured using machine-interpretable semantics to fully benefit from this transformation. This is challenging, as annota- tors’ requirements are heterogeneous, varying both across different types of user (e.g., musician, scholar) and within these groups, de- pending on the specific use-case. A hypothetical all-encompassing tool catering to every conceivable annotation type, even if it were possible to build, would vastly complicate user interaction. This additional complexity would significantly increase cognitive load and impair usability, particularly in dynamic real-time usage con- texts, e.g., live annotation during music rehearsal or performance. To address this challenge, we present a social data infrastructure that facilitates the creation of use-case specific annotation toolkits. Its components include a selectable-score module that supports customisable click-and-drag selection of score elements (e.g., notes, measures, directives); the Web Annotations data model, extended to support the creation of custom, Web-addressable annotation types supporting the specification and (re-)use of annotation palettes; and the Music Encoding and Linked Data (MELD) Javascript client library, used to build interfaces that map annotation types to render- ing and interaction handlers. We have extended MELD to support the Solid platform for social Linked Data, allowing annotations to be privately stored in user-controlled Personal Online Datastores (Pods), or selectively shared or published. To demonstrate the feasi- bility of our proposed approach, we present annotation interfaces employing the outlined infrastructure in three distinct use-cases: scholarly communication; music rehearsal; and rating during music listening

An Experimental Analysis of Nash Refinements in Signaling Games

This paper investigates the refinements of Nash equilibrium in two person signaling game experiments. The experimental games cover the watershed of the nested refinements: Bayes-Nash, Sequential, Intuitive, Divine, Universally Divine, NWBR, and Stabel. In each game an equilibrium selection problem is defined in which adjacent refinements are considered. The pattern of outcomes suggest that individuals select the more refined equilibria up to the divinity concept. However, an anomaly occurs in the game in which the stable equilibrium is a clear preference among the subjects. Since the concepts are nested this suggests that the outcomes are game specific. Sender behavior does not seem to follow any specific decision rule (e.g., Nash, minmax, PIR, etc.) while receiver actions tend to correspond to the Nash equilibrium outcomes

Enrichment of pathogenic alleles in the brittle cornea gene, ZNF469, in keratoconus

Keratoconus, a common inherited ocular disorder resulting in progressive corneal thinning, is the leading indication for corneal transplantation in the developed world. Genome-wide association studies have identified common SNPs 100 kb upstream of ZNF469 strongly associated with corneal thickness. Homozygous mutations in ZNF469 and PR domain-containing protein 5 (PRDM5) genes result in brittle cornea syndrome (BCS) Types 1 and 2, respectively. BCS is an autosomal recessive generalized connective tissue disorder associated with extreme corneal thinning and a high risk of corneal rupture. Some individuals with heterozygous PRDM5 mutations demonstrate a carrier ocular phenotype, which includes a mildly reduced corneal thickness, keratoconus and blue sclera. We hypothesized that heterozygous variants in PRDM5 and ZNF469 predispose to the development of isolated keratoconus. We found a significant enrichment of potentially pathologic heterozygous alleles in ZNF469 associated with the development of keratoconus (P = 0.00102) resulting in a relative risk of 12.0. This enrichment of rare potentially pathogenic alleles in ZNF469 in 12.5% of keratoconus patients represents a significant mutational load and highlights ZNF469 as the most significant genetic factor responsible for keratoconus identified to date