Canopy-associated arthropods in Acacia koa and Metrosideros tree communities along the Mauna loa Transect

Reports were scanned in black and white at a resolution of 600 dots per inch and were converted to text using Adobe Paper Capture Plug-in.The spatial distribution and zonation of canopy-associated arthropods of Acacia koa and Metrosideros tree communities along an altitudinal transect on the east flank of Mauna Loa was determined by insecticidal fogging of the canopy with pyrethrum. Eight sites were on the Mauna Loa Transect, which has been intensively sampled by IBP participants in the Island Ecosystems IRP. Two sets of transect zones were determined on the basis of arthropod distribution. The influence of environmental and biotic factors, plant community structure and climate are interpreted according to distribution patterns. The distribution of arthropod groups coincided quite closely with vascular plant communities of the transect as defined by other studies. The composition, spatial distribution, and environmental relationships of arthropod canopy communities along the Mauna Loa Transect are compared with the situation pertaining along other lower elevational transects to sea level in Hawaii Volcanoes National Park as well as with other ecosystems in order to further characterize the arthropod canopy community. Host specificity, vegetation structure, competition between ecological homologs, and climate appeared to have the most important influence on population density and spatial distribution patterns of the arthropod taxa studied.I thank the following people for their time and enthusiasm in helping with data analysis, manuscript evaluation, and discussion of results: K. W. Bridges, G. V. Carey, F.G. Howarth, D. Mueller-Dombois and G. Nakahashi. I am especially grateful to B. Dalton, J. D. Jacobi, B. Furmidge, and T. T. Parman for their invaluable assistance in the field, and to B. Dalton for his assistance in life-history analysis of some arthropods. For their expert identification of many of the arthropods, I am indebted to: P. F. Bellinger (Collembola), C. W. O'Brien (Curculionidae and Proterhinidae), L. B. O'Brien (Fulgoroidea), F. G. Howarth (especially Diptera), J. F. Lac1rence (Ciidae), J. R. Leeper (Cocdnellidae), K. Sakimura (Thysanoptera), G. A. Samuelson (various Coeoptera), W. A. Steffan (Sciaridae) and J. M. Tenorio (Collembola and Dolichopodidae). Ibby Harrison typed the rough drafts of this manuscript for which I am also grateful

Altitudinal distribution and composition of anthropods in 'Ohi'a (Metrosideros Collina subsp. Polymorpha) canopies in Hawaii Volcanoes National Park with ecological implications for some native biota

Western Region, National Park Servic

Spatially-Resolved Spectra of the "Teacup" AGN: Tracing the History of a Dying Quasar

The Sloan Digital Sky Survey (SDSS) Galaxy Zoo project has revealed a number of spectacular galaxies possessing Extended Emission-Line Regions (EELRs), the most famous being Hanny's Voorwerp galaxy. We present another EELR object discovered in the SDSS endeavor: the Teacup Active Galactic Nucleus (AGN), nicknamed for its EELR, which has a handle like structure protruding 15 kpc into the northeast quadrant of the galaxy. We analyze physical conditions of this galaxy with long-slit ground based spectroscopy from Lowell, Lick, and KPNO observatories. With the Lowell 1.8m Perkin's telescope we took multiple observations at different offset positions, allowing us to recover spatially resolved spectra across the galaxy. Line diagnostics indicate the ionized gas is photoionized primarily by the AGN. Additionally we are able to derive the hydrogen density from the [S II] 6716/6731 ratio. We generated two-component photoionization models for each spatially resolved Lowell spectrum. These models allow us to calculate the AGN bolometric luminosity seen by the gas at different radii from the nuclear center of the Teacup. Our results show a drop in bolometric luminosity by more than two orders of magnitude from the EELR to the nucleus, suggesting that the AGN has decreased in luminosity by this amount in a continuous fashion over 46,000 years, supporting the case for a dying AGN in this galaxy independent of any IR based evidence. We demonstrate that spatially resolved photoionization modeling could be applied to EELRs to investigate long time scale variability.Comment: 38 pages, 11 figures, accepted for publication in the Astrophysical Journa

The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column.

Plastic waste has been documented in nearly all types of marine environments and has been found in species spanning all levels of marine food webs. Within these marine environments, deep pelagic waters encompass the largest ecosystems on Earth. We lack a comprehensive understanding of the concentrations, cycling, and fate of plastic waste in sub-surface waters, constraining our ability to implement effective, large-scale policy and conservation strategies. We used remotely operated vehicles and engineered purpose-built samplers to collect and examine the distribution of microplastics in the Monterey Bay pelagic ecosystem at water column depths ranging from 5 to 1000 m. Laser Raman spectroscopy was used to identify microplastic particles collected from throughout the deep pelagic water column, with the highest concentrations present at depths between 200 and 600 m. Examination of two abundant particle feeders in this ecosystem, pelagic red crabs (Pleuroncodes planipes) and giant larvaceans (Bathochordaeus stygius), showed that microplastic particles readily flow from the environment into coupled water column and seafloor food webs. Our findings suggest that one of the largest and currently underappreciated reservoirs of marine microplastics may be contained within the water column and animal communities of the deep sea

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and sub-grid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate timescales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system. We also find that in general those models which produce skillful forecasts are also associated with the best climate simulations.Comment: Submitted to Journal of Advances in Modeling Earth Systems (JAMES