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Abstract Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non‐model systems has also enabled a shift away from population‐based sampling to individual‐based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual‐based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population‐based sampling to individual‐based sampling schemes. Here, we discuss the benefits of individual‐based sampling for conservation and describe how landscape genomic methods, paired with individual‐based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user‐friendly, open‐source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). Thealgatrpackage includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology. 

We study subtrajectory clustering under the Fréchet distance. Given one or more trajectories, the task is to split the trajectories into several parts, such that the parts have a good clustering structure. We approach this problem via a new set cover formulation, which we think provides a natural formalization of the problem as it is studied in many applications. Given a polygonal curve P with n vertices in fixed dimension, integers k, ℓ ≥ 1, and a real value Δ > 0, the goal is to find k center curves of complexity at most ℓ such that every point on P is covered by a subtrajectory that has small Fréchet distance to one of the k center curves (≤ Δ). In many application scenarios, one is interested in finding clusters of small complexity, which is controlled by the parameter ℓ. Our main result is a bicriterial approximation algorithm: if there exists a solution for given parameters k, ℓ, and Δ, then our algorithm finds a set of k' center curves of complexity at most ℓ with covering radius Δ' with k' in O(kℓ2 log (kℓ)), and Δ' ≤ 19Δ. Moreover, within these approximation bounds, we can minimize k while keeping the other parameters fixed. If ℓ is a constant independent of n, then, the approximation factor for the number of clusters k is O(log k) and the approximation factor for the radius Δ is constant. In this case, the algorithm has expected running time in Õ(km2 + mn) and uses space in O(n + m), where m=⌈L/Δ⌉ and L is the total arclength of the curve P. 

Abstract Genetic diversity plays a key role in maintaining population viability by preventing inbreeding depression and providing the building blocks for adaptation. Understanding how genetic diversity varies across space is, therefore, of key interest in conservation and population genetics.Here, we introducewingen, anrpackage for calculating continuous maps of genetic diversity, including nucleotide diversity, allelic richness, and heterozygosity, from standard genotypic and spatial data using a spatial moving window approach. We provide functions to account for variation in sample size across space using rarefaction, to create kriging‐interpolated maps of genetic diversity, and to mask any areas that are outside the area of interest.Tests with simulated and empirical datasets demonstrate thatwingencan successfully capture variation in genetic diversity across landscapes from both reduced‐representation and whole genome sequencing datasets. For reduced‐representation datasets,wingen's functions can be run easily on a standard laptop computer, and we provide options for parallelization to increase the efficiency of running larger whole genome datasets.wingenprovides novel and computationally tractable tools for creating informative maps of genetic diversity with applications for conservation prioritization as well as population and landscape genetic analyses. 

Context. Ionized interstellar gas is an important component of the interstellar medium and its lifecycle. The recent evidence for a widely distributed highly ionized warm interstellar gas with a density intermediate between the warm ionized medium (WIM) and compact H  II regions suggests that there is a major gap in our understanding of the interstellar gas. Aims. Our goal is to investigate the properties of the dense WIM in the Milky Way using spectrally resolved SOFIA GREAT [N  II ] 205 μm fine-structure lines and Green Bank Telescope hydrogen radio recombination lines (RRL) data, supplemented by spectrally unresolved Herschel PACS [N  II ] 122μm data, and spectrally resolved 12 CO. Methods. We observed eight lines of sight (LOS) in the 20° < l < 30° region in the Galactic plane. We analyzed spectrally resolved lines of [N  II ] at 205 μm and RRL observations, along with the spectrally unresolved Herschel PACS 122 μm emission, using excitation and radiative transfer models to determine the physical parameters of the dense WIM. We derived the kinetic temperature, as well as the thermal and turbulent velocity dispersions from the [N  II ] and RRL linewidths. Results. The regions with [N  II ] 205 μm emission are characterized by electron densities, n ( e ) ~ 10−35 cm −3 , temperatures range from 3400 to 8500 K, and nitrogen column densities N(N + ) ~ 7 × 10 16 to 3 × 10 17 cm −2 . The ionized hydrogen column densities range from 6 × 10 20 to 1.7 × 10 21 cm −2 and the fractional nitrogen ion abundance x (N + ) ~ 1.1 × 10 −4 to 3.0 × 10 −4 , implying an enhanced nitrogen abundance at a distance ~4.3 kpc from the Galactic Center. The [N  II ] 205 μm emission lines coincide with CO emission, although often with an offset in velocity, which suggests that the dense warm ionized gas is located in, or near, star-forming regions, which themselves are associated with molecular gas. Conclusions. These dense ionized regions are found to contribute ≳50% of the observed [C  II ] intensity along these LOS. The kinetic temperatures we derive are too low to explain the presence of N + resulting from electron collisional ionization and/or proton charge transfer of atomic nitrogen. Rather, these regions most likely are ionized by extreme ultraviolet (EUV) radiation from nearby star-forming regions or as a result of EUV leakage through a clumpy and porous interstellar medium. 

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is ${}^{136}\mathrm{Xe}$, which would double beta decay into ${}^{136}\mathrm{Ba}$. Detecting the single ${}^{136}\mathrm{Ba}$daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon. Published by the American Physical Society2024 

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double- $\beta$decay detector ( $\sim 5$metric ton, 90% ${}^{136}\mathrm{Xe}$, 10% ${}^{134}\mathrm{Xe}$) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the Model of Argon Reaction Low Energy Yields (MARLEY) event generator. We find good agreement between MARLEY’s predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector’s efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5–8 kpc from Earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae. Published by the American Physical Society2024