Matrix isolation study of the photolysis of cyanogen azide. The infrared and ultraviolet spectra of the free radical NCN

Infrared and ultraviolet absorption spectra of free radical in photolysis of cyanogen azid

Matrix isolation study of the reaction of f atoms with co infrared and ultraviolet spectrum of the free radical fco

Matrix isolation of reaction of fluorine atoms with carbon monoxide - infrared and ultraviolet spectrum of free radical fluorocarbon monoxid

Mouth development

A mouth is present in all animals, and comprises an opening from the outside into the oral cavity and the beginnings of the digestive tract to allow eating. This review focuses on the earliest steps in mouth formation. In the first half, we conclude that the mouth arose once during evolution. In all animals, the mouth forms from ectoderm and endoderm. A direct association of oral ectoderm and digestive endoderm is present even in triploblastic animals, and in chordates, this region is known as the extreme anterior domain (EAD). Further support for a single origin of the mouth is a conserved set of genes that form a 'mouth gene program' including foxA and otx2. In the second half of this review, we discuss steps involved in vertebrate mouth formation, using the frog Xenopus as a model. The vertebrate mouth derives from oral ectoderm from the anterior neural ridge, pharyngeal endoderm and cranial neural crest (NC). Vertebrates form a mouth by breaking through the body covering in a precise sequence including specification of EAD ectoderm and endoderm as well as NC, formation of a 'pre-mouth array,' basement membrane dissolution, stomodeum formation, and buccopharyngeal membrane perforation. In Xenopus, the EAD is also a craniofacial organizer that guides NC, while reciprocally, the NC signals to the EAD to elicit its morphogenesis into a pre-mouth array. Human mouth anomalies are prevalent and are affected by genetic and environmental factors, with understanding guided in part by use of animal models. WIREs Dev Biol 2017, 6:e275. doi: 10.1002/wdev.275 For further resources related to this article, please visit the WIREs website

Identification of H2_2CCC as a diffuse interstellar band carrier

We present strong evidence that the broad, diffuse interstellar bands (DIBs) at 4881 and 5450\,\AA are caused by the B\,^1B$_1$\,$\leftarrow$\,X\,^1A$_1$ transition of H$_2$CCC (l-C$_3$H$_2$). The large widths of the bands are due to the short lifetime of the B\,^1B$_1$ electronic state. The bands are predicted from absorption measurements in a neon matrix and observed by cavity ring-down in the gas phase and show exact matches to the profiles and wavelengths of the two broad DIBs. The strength of the 5450\,\AA DIB leads to a l-C$_3$H$_2$ column density of $\sim5\times10^{14}$ cm$^{-2}$ towards HD\,183143 and $\sim2\times10^{14}$\,cm$^{-2}$ to HD\,206267. Despite similar values of $E$($B-V$), the 4881 and 5450\,\AA DIBs in HD\,204827 are less than one third their strength in HD\,183143, while the column density of interstellar C$_3$ is unusually high for HD\,204827 but undetectable for HD\,183143. This can be understood if C$_3$ has been depleted by hydrogenation to species such as l-C$_3$H$_2$ towards HD\,183143. There are also three rotationally resolved sets of triplets of l-C$_3$H$_2$ in the 6150$-$6330\,\AA region. Simulations, based on the derived spectroscopic constants and convolved with the expected instrumental and interstellar line broadening, show credible coincidences with sharp, weak DIBs for the two observable sets of triplets. The region of the third set is too obscured by the $\alpha$-band of telluric O$_2$.Comment: 22 pages, 9 figure

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest

Performance Evaluation of Cetacean Species Distribution Models Developed Using Generalized Additive Models and Boosted Regression Trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest

Future change of summer hypoxia in coastal California Current

The occurrences of summer hypoxia in coastal California Current can significantly affect the benthic and pelagic habitat and lead to complex ecosystem changes. Model-simulated hypoxia in this region is strongly spatially heterogeneous, and its future changes show uncertainties depending on the model used. Here, we used an ensemble of the new generation Earth system models to examine the present-day and future changes of summer hypoxia in this region. We applied model-specific thresholds combined with empirical bias adjustments of the dissolved oxygen variance to identify hypoxia. We found that, although simulated dissolved oxygen in the subsurface varies across the models both in mean state and variability, after necessary bias adjustments, the ensemble shows reasonable hypoxia frequency compared with a hindcast in terms of spatial distribution and average frequency in the coastal region. The models project increases in hypoxia frequency under warming, which is in agreement with deoxygenation projected consistently across the models for the coastal California Current. This work demonstrated a practical approach of using the multi-model ensemble for regional studies while presenting methodology limitations and gaps in observations and models to improve these limitations

Fit to Predict? Ecoinformatics for Predicting the Catchability of a Pelagic Fish in Near Real-Time

The ocean is a dynamic environment inhabited by a diverse array of highly migratory species, many of which are under direct exploitation in targeted fisheries. The timescales of variability in the marine realm coupled with the extreme mobility of ocean-wandering species such as tuna and billfish complicates fisheries management. Developing ecoinformatics solutions that allow for near real-time prediction of the distributions of highly mobile marine species is an important step towards the maturation of dynamic ocean management and ecological forecasting. Using 25 years (1990-2014) of NOAA fisheries\u27 observer data from the California drift gillnet fishery, we model relative probability of occurrence (presence-absence) and catchability (total catch) of broadbill swordfish Xiphias gladius in the California Current System (CCS). Using freely-available environmental datasets and open source software, we explore the physical drivers of regional swordfish distribution. Comparing models built upon remotely-sensed datasets with those built upon a data-assimilative configuration of the Regional Ocean Modelling System (ROMS), we explore trade-offs in model construction and address how physical data can affect predictive performance and operational capacity. Swordfish catchability was found to be highest in deeper waters (\u3e1500m) with surface temperatures in the 14-20 degrees C range, isothermal layer depth (ILD) of 20-40m, positive sea surface height anomalies and during the new moon

Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks lsurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (\u3c 100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models