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Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes
There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.
The SkyView Data Model: Handling Heterogeneous Data Using Dynamic Software
The SkyView Virtual Telescope allows users to query over 75 distinct datasets of astronomical imagery including more than 190 distinct surveys. These holdings are extraordinarily diverse ranging over more than 15 orders of magnitude in frequency and5 orders of magnitude in resolution. To accommodate supporting these data, SkyView has developed a simple data model for surveys. Nor surprisingly, given the diverse origins of the datasets included, some surveys do not easily fit within this model. The conventional approach to dealing with such outliers is to build a layer on top of the actual data which more faithfully adheres to the data model. With SkyView we have used a somewhat alternative approach. We allow the data model to define new behaviors in the SkyView code itself either by customizing the use of existing capabilities or pointing to new classes that have non-standard behavior. This approach is feasible since in modern computer languages the code that is run can be selected dynamically with new classes completely overriding traditional behavior. This paper describes this approach and considers how it may become more useful as our software structures become increasingly dynamically linked.
Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar
Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.
The Extraordinary Passage of Comet C/2020 F3 NEOWISE: Evidence for Heterogeneous Chemical Inventory in Its Nucleus
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Incorporating Effects of Subgrid Surface Heterogeneity in a Multi-Plume EDMF Parameterization
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Testing the role of isotopic composition and heterogeneity in the heat tolerance of bacterial spores
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Global geologic map of asteroid (101955) Bennu indicates heterogeneous resurfacing in the past 500,000 years
Global geologic maps are useful tools for efficient interpretation of a planetary body, and they provide global context for the diversity and evolution of the surface. We used data acquired by the OSIRIS-REx spacecraft to create the first global geologic map of the near-Earth asteroid (101955) Bennu. As this is the first geologic map of a small, non-spherical, rubble-pile asteroid, we discuss the distinctive mapping challenges and best practices that may be useful for future exploration of similar asteroids, such as those to be visited with the Hera and Janus missions. By mapping on two centimeter-scale global image mosaics (2D projected space) and a centimeter-scale global shape model (3D space), we generated three input maps respectively describing Bennu’s shape features, geologic features, and surface texture. Based on these input maps, we defined two geologic units: the Smooth Unit and the Rugged Unit. The units are differentiated primarily on the basis of surface texture, concentrations of boulders, and the distributions of lineaments, mass movement features, and craters. They are bounded by several scarps. The Rugged Unit contains abundant boulders and signs of recent mass movement. It also has fewer small(<20 m), putatively fresh craters than the Smooth Unit, suggesting that such craters have been erased in the former. Based on these geologic indicators, we infer that the Rugged Unit has the younger surface of the two. Differential crater size-frequency distributions and the distribution of the freshest craters suggest that both unit surfaces formed ~10–65 million years ago, when Bennu was located in the Main Asteroid Belt, and the Smooth Unit has not been significantly resurfaced in the past 2 million years. Meanwhile, the Rugged Unit has experienced resurfacing within the past ~500,000years during Bennu’s lifetime as a near-Earth asteroid. The geologic units are consistent with global diversity in slope, surface roughness, normal albedo, and thermal emission spectral characteristics. The site on Bennu where the OSIRIS-REx mission collected a regolith sample is located in the Smooth Unit, in a small crater nested within a larger one. So although the Smooth Unit is an older surface than the Rugged Unit, the impact-crater settingindicates that the material sampled was recently exposed. Several similarities are apparent between Bennu and asteroid (162173) Ryugu from a global geologic perspective, including two geologic units distinguishable by variations in the number density of boulders, as well as in other datasets such as brightness.
Representing the Sub-Grid Heterogeneity of Surface Precipitation in A General Circulation Model
Precipitation variability on spatial scales smaller than a typical general circulation model (GCM) grid box is often neglected, with the grid-mean precipitation rate being applied uniformly to underlying surface tiles. This reduces the extrema seen by the surface, with corresponding reductions in surface runoff and altered land-atmosphere fluxes. Here we present a novel approach to stochastically distribute precipitation across sub-grid surface tiles within a GCM. Based on 4 km Stage IV precipitation data, the scheme parameterizes the dry area fraction as a function of grid mean precipitation rate, and defines the relative distribution of intensities across non-dry surface tiles. To incorporate memory and mimic the persistence of precipitating storms, the relative intensity assigned to each sub-grid tile is determined by an autoregressive process. Using single column experiments, the scheme is shown to reproduce observed precipitation statistics at the scale of model surface tiles. We also document impacts on surface hydrology and energy partitioning, with notable increases in precipitation runoff, surface temperature variance, and the Bowen ratio.
Early History of the Solar System Revealed By Calcium-48 Heterogeneity in Calcium-Aluminium-Rich Inclusions
Introduction: Calcium-aluminum-rich inclusions (CAIs) are the earliest formed solids in the solar system. Although the mineralogy of CAIs is consistent with condensation from a solar composition gas, textural, elemental and isotopic signatures point to CAIs having more complicated thermal histories rather than simply being primary condensates. Untangling the condensation record in these refractory inclusions and the associated physicochemical environment, can provide insights into temporal and/or spatial reservoirs in the protoplanetary disk where solids were forming.
Performance Optimization of Automatic Differentiation for Unstructured-Grid CFD Applications on Emerging Heterogeneous Architectures
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