Past and present distribution, densities and movements of blue whales <i>Balaenoptera musculus</i> in the Southern Hemisphere and northern Indian Ocean

1Blue whale locations in the Southern Hemisphere and northern Indian Ocean were obtained from catches (303 239), sightings (4383 records of =8058 whales), strandings (103), Discovery marks (2191) and recoveries (95), and acoustic recordings.2Sighting surveys included 7 480 450 km of effort plus 14 676 days with unmeasured effort. Groups usually consisted of solitary whales (65.2%) or pairs (24.6%); larger feeding aggregations of unassociated individuals were only rarely observed. Sighting rates (groups per 1000 km from many platform types) varied by four orders of magnitude and were lowest in the waters of Brazil, South Africa, the eastern tropical Pacific, Antarctica and South Georgia; higher in the Subantarctic and Peru; and highest around Indonesia, Sri Lanka, Chile, southern Australia and south of Madagascar.3Blue whales avoid the oligotrophic central gyres of the Indian, Pacific and Atlantic Oceans, but are more common where phytoplankton densities are high, and where there are dynamic oceanographic processes like upwelling and frontal meandering.4Compared with historical catches, the Antarctic (‘true’) subspecies is exceedingly rare and usually concentrated closer to the summer pack ice. In summer they are found throughout the Antarctic; in winter they migrate to southern Africa (although recent sightings there are rare) and to other northerly locations (based on acoustics), although some overwinter in the Antarctic.5Pygmy blue whales are found around the Indian Ocean and from southern Australia to New Zealand. At least four groupings are evident: northern Indian Ocean, from Madagascar to the Subantarctic, Indonesia to western and southern Australia, and from New Zealand northwards to the equator. Sighting rates are typically much higher than for Antarctic blue whales.6South-east Pacific blue whales have a discrete distribution and high sighting rates compared with the Antarctic. Further work is needed to clarify their subspecific status given their distinctive genetics, acoustics and length frequencies.7Antarctic blue whales numbered 1700 (95% Bayesian interval 860–2900) in 1996 (less than 1% of original levels), but are increasing at 7.3% per annum (95% Bayesian interval 1.4–11.6%). The status of other populations in the Southern Hemisphere and northern Indian Ocean is unknown because few abundance estimates are available, but higher recent sighting rates suggest that they are less depleted than Antarctic blue whales.</li

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

2014 Lidar-Derived 1m Digital Elevation Model Data Set for Reynolds Creek Experimental Watershed, Southwestern Idaho

Full waveform lidar data were collected by NASA\u27s Jet propulsion Laboratory (JPL) Airborne Snow Observatory (ASO) in August 2014 for a NASA Terretrial Ecology project (NNX14AD81G). The data were collected using a Riegl LMS Q-1560 dual laser scanner system. The full waveforms were decomposed in Riegl RiPROCESS software to generate 3D point cloud with an average point density of 14-20 pts/m2. The point clouds were corrected for elevation and roll misalignment between adjacent flight lines in TerraScan. A Digital Elevation Model (DEM) of 1 m resolution was derived using the corrected point cloud using BCAL Lidar Tools (https://bcal.boisestate.edu/tools/lidar and https://github.com/bcal-lidar/tools/wiki/BareDEM)

Scanned–array audio beamforming using 2nd− and 3rd–order 2D IIR beam filters on FPGA

Real-time scanned-array direct-form-I hardware implementations of two-dimensional (2D) infinite impulse response (IIR) frequency-planar beam plane-wave (PW) filters have potentially wide applications in the directional enhancement of spatio-temporal broadband PWs based on their directions of arrival (DOAs). The proposed prototypes consist of a microphone sensor array, low-noise-amplifiers (LNAs), multiplexers (MUXs), a programmable gain amplifier (PGA), an analog to digital converter (ADC), a digital to analog converter (DAC), and a field programmable gate array (FPGA) circuit based 2D IIR spatiotemporal beam filter implemented on a single Xilinx Virtex2P xc2vp30-7ff896 FPGA chip. Starting from published 1st-order designs, novel FPGA architectures for highly-selective 2nd- and 3rd-order beam PW filters are proposed, simulated, implemented on FPGA, and verified on-chip

Constraining Plant Functional Types in a Semi-Arid Ecosystem with Waveform Lidar

Accurate classification of plant functional types (PFTs) reduces the uncertainty in global biomass and carbon estimates. Airborne small-footprint waveform lidar data are increasingly used for vegetation classification and above-ground carbon estimates at a range of spatial scales in woody or homogeneous grass and savanna ecosystems. However, a gap remains in understanding how waveform features represent and ultimately can be used to constrain the PFTs in heterogeneous semi-arid ecosystems. This study evaluates lidar waveform features and classification performance of six major PFTs, including shrubs and trees, along with bare ground in the Reynolds Creek Experimental Watershed, Idaho, USA. Waveform lidar data were obtained with the NASA Airborne Snow Observatory (ASO). From these data we derived waveform features at two spatial scales (1 m and 10 m rasters) by applying a Gaussian decomposition and a frequency-domain deconvolution. An ensemble random forest algorithm was used to assess classification performance and to select the most important waveform features. Classification models developed with the 10 m waveform features outperformed those at 1 m (Kappa (κ) = 0.81–0.86 vs. 0.60–0.70, respectively). At 1 m resolution, lidar height features improved the PFT classification accuracy by 10% compared to the analysis without these features. However, at 10 m resolution, the inclusion of lidar derived heights with other waveform features decreased the PFT classification performance by 4%. Pulse width, rise time, percent energy, differential target cross section, and radiometrically calibrated backscatter coefficient were the most important waveform features at both spatial scales. A significant finding is that bare ground was clearly differentiated from shrubs using pulse width. Though the overall accuracy ranges between 0.72 and 0.89 across spatial scales, the two shrub PFTs showed 0.45–0.87 individual classification success at 1 m, while bare ground and tree PFTs showed high (0.72–1.0) classification accuracy at 10 m. We conclude that small-footprint waveform features can be used to characterize the heterogeneous vegetation in this and similar semi-arid ecosystems at high spatial resolution. Furthermore, waveform features such as pulse width can be used to constrain the uncertainty of terrain modeling in environments where vegetation and bare ground lidar returns are close in time and space. The dependency on spatial resolution plays a critical role in classifi- cation performance in tree-shrub co-dominant ecosystems

Sri Lankan Medical Undergraduates Awareness of Nanotechnology and Its Risks

This study examines students’ understanding of the normative connections between key concepts of nanotechnology in nanomedicine and underlying biological principles that are critical for an in-depth understanding of its therapeutic application in medical field. A structured questionnaire was distributed among randomly selected undergraduates at the Faculty of Medicine and Allied Sciences, University of Rajarata, Sri Lanka. A total of 80 students participated in this study and completed written questionnaire on nanomedicine. The outcome of this study shows that there is a strong positive response on basic knowledge on nanoscale, but the undergraduates had an average knowledge on therapeutic application related to nanomedicine. Almost all students had a good knowledge on nanoscale but they lack knowledge of the relationship between nano and nanomedicine. Specifically, students were challenged to demonstrate an integrated understanding of the nanomedicine therapeutic application. Almost 58% of them were unable to give an example of it. Also some students struggled to explain it. Furthermore, in this study it was observed that there is a positive correlation in risk benefit section related to nanomedicine. Although the outcome is preliminary in nature, the results provide cause for concern over the status of nanotechnology education in Sri Lanka which needed to be uplifted

Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation — across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building