Estimates of Marine Mammal, Sea Turtle, and Seabird Mortality in the California Drift Gillnet Fishery for Swordfish and Thresher Shark, 1996–2002

Estimates of incidental marine mammal, sea turtle, and seabird mortality in the California drift gillnet fishery for broadbill swordfish, Xiphias gladius, and common thresher shark, Alopias vulpinus, are summarized for the 7-year period, 1996 to 2002. Fishery observer coverage was 19% over the period (3,369 days observed/17,649 days fished). An experiment to test the effectiveness of acoustic pingers on reducing marine mammal entanglements in this fishery began in 1996 and resulted in statistically significant reductions in marine mammal bycatch. The most commonly entangled marine mammal species were the short-beaked common dolphin, Delphinus delphis; California sea lion, Zalophus californianus; and northern right whale dolphin, Lissodelphis borealis. Estimated mortality by species (CV and observed mortality in parentheses) from 1996 to 2002 is 861 (0.11, 133) short-beaked common dolphins; 553 (0.16, 103) California sea lions; 151 (0.25, 31) northern right whale dolphins; 150 (0.21, 27) northern elephant seals, Mirounga angustirostris; 54 (0.41, 10) long-beaked common dolphins, Delphinus capensis; 44 (0.53, 6) Dall’s porpoise, Phocoenoides dalli; 19 (0.60, 5) Risso’s dolphins, Grampus griseus; 11 (0.71, 2) gray whales, Eschrichtius robustus; 7 (0.83, 2) sperm whales, Physeter macrocephalus; 7 (0.96, 1) short-finned pilot whales, Globicephala macrorhychus; 12 (1.06, 1) minke whales, Balaenoptera acutorostrata; 5 (1.05, 1) fin whales, Balaenoptera physalus; 11 (0.68, 2) unidentified pinnipeds; 33 (0.52, 4) leatherback turtles, Dermochelys coriacea; 18 (0.57, 3) loggerhead turtles, Caretta caretta; 13 (0.73, 3) northern fulmars, Fulmarus glacialis; and 6 (0.86, 2) unidentified birds

Interpreting Dark Matter Direct Detection Independently of the Local Velocity and Density Distribution

We demonstrate precisely what particle physics information can be extracted from a single direct detection observation of dark matter while making absolutely no assumptions about the local velocity distribution and local density of dark matter. Our central conclusions follow from a very simple observation: the velocity distribution of dark matter is positive definite, f(v) >= 0. We demonstrate the utility of this result in several ways. First, we show a falling deconvoluted recoil spectrum (deconvoluted of the nuclear form factor), such as from ordinary elastic scattering, can be "mocked up" by any mass of dark matter above a kinematic minimum. As an example, we show that dark matter much heavier than previously considered can explain the CoGeNT excess. Specifically, m_chi < m_Ge} can be in just as good agreement as light dark matter, while m_\chi > m_Ge depends on understanding the sensitivity of Xenon to dark matter at very low recoil energies, E_R ~ 6 keVnr. Second, we show that any rise in the deconvoluted recoil spectrum represents distinct particle physics information that cannot be faked by an arbitrary f(v). As examples of resulting non-trivial particle physics, we show that inelastic dark matter and dark matter with a form factor can both yield such a rise

PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform

1. Studying fungal biodiversit y using data generated from Illumina MiSeq sequencing platforms poses a number of bioinformatic challenges with the analysis typically involving a large number of tools for each analytical step from quality filtering to generating identified operational taxonomic unit (OTU) abundance tables. 2. Here, we introduce PIPITS, an o pen-source stand-alone suite of software for automated processing of Illumina MiSeq sequences for fungal community analysis. PIPITS e xploits a number of state of the art applications to process paired-end reads from quality filtering to producing OTU abundance tables. 3. We pro vide detailed descriptions of the pipeline and show its utility in the analysis of 9 396 092 sequences generated on the MiSeq platform from Illumina MiSeq. 4. PIPITS is the first automated bioinformatics pipeline dedicated for fungal ITS sequences which incorporates ITSx to extract subregions of ITS and exploits the latest RDP Classifier to classify sequences against the curatedUNITE fungal data set

A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest

Joint analysis of stressors and ecosystem services to enhance restoration effectiveness

With increasing pressure placed on natural systems by growing human populations, both scientists and resource managers need a better understanding of the relationships between cumulative stress from human activities and valued ecosystem services. Societies often seek to mitigate threats to these services through large-scale, costly restoration projects, such as the over one billion dollar Great Lakes Restoration Initiative currently underway. To help inform these efforts, we merged high-resolution spatial analyses of environmental stressors with mapping of ecosystem services for all five Great Lakes. Cumulative ecosystem stress is highest in near-shore habitats, but also extends offshore in Lakes Erie, Ontario, and Michigan. Variation in cumulative stress is driven largely by spatial concordance among multiple stressors, indicating the importance of considering all stressors when planning restoration activities. In addition, highly stressed areas reflect numerous different combinations of stressors rather than a single suite of problems, suggesting that a detailed understanding of the stressors needing alleviation could improve restoration planning. We also find that many important areas for fisheries and recreation are subject to high stress, indicating that ecosystem degradation could be threatening key services. Current restoration efforts have targeted high-stress sites almost exclusively, but generally without knowledge of the full range of stressors affecting these locations or differences among sites in service provisioning. Our results demonstrate that joint spatial analysis of stressors and ecosystem services can provide a critical foundation for maximizing social and ecological benefits from restoration investments. www.pnas.org/lookup/suppl/doi:10.1073/pnas.1213841110/-/DCSupplementa

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software