The majority of bird and bat species are incapable of carrying tags that transmit their position to satellites. Given fundamental power requirements for such communication, burdened mass guidelines and battery technology, this constraint necessitates the continued use of very high frequency (VHF) radio beacons. As such, efforts should be made to mitigate their primary deficiencies: detection range, localization time and localization accuracy. The integration of a radiotelemetry system with an unmanned aerial vehicle (UAV) could significantly improve the capacity for data collection from VHF tags. We present a UAV‐integrated radiotelemetry system that relies on open source hardware and software. Localization methods, including signal processing, bearing estimation based on principal component analysis, localization techniques and test results, are discussed. Using a low‐power beacon applicable for bats and small birds, testing showed that the improved vantage of the UAV‐radiotelemetry system (UAV‐RT) provided significantly higher received signal power compared to the low‐level flights (maximum range beyond 1.4 km). Flight testing of localization methods showed median bearing errors between 2.3° and 6.8°, with localization errors of between 5% and 14% of the distance to the tag. In a direct comparison to an experienced radiotelemetry user, the UAV‐RT system provided bearing and localization estimates with 53% less error. This paper introduces the core functionality and use methods of the UAV‐RT system, while presenting baseline localization performance metrics. An associated website hosts plans for assembly and software installation. The methods of UAV‐RT use for tag detection will be further developed in future works. For both the detection and localization problems, the mobility of a flying asset drastically reduces tracker time requirements. A 7‐min flight would be sufficient to collect five equally spaced bearing estimates over a 1‐km transect. The use of a software‐defined radio on the UAV‐RT system will allow for the simultaneous detection and localization of multiple tags.
Erroneous data can creep into sequence datasets for reasons ranging from contamination to annotation and alignment mistakes and reduce the accuracy of downstream analyses. As datasets keep getting larger, it has become difficult to check multiple sequence alignments visually for errors, and thus, automatic error detection methods are needed more than ever before. Alignment masking methods, which are widely used, remove entire aligned sites and may reduce signal as much as or more than they reduce the noise. The alternative we propose here is a surprisingly under‐explored approach: looking for errors in small species‐specific stretches of the multiple sequence alignments. We introduce a method called TAPER that uses a novel two‐dimensional outlier detection algorithm. Importantly, TAPER adjusts its null expectations per site and species, and in doing so, it attempts to distinguish the real heterogeneity (signal) from errors (noise). Our results show that TAPER removes very little data yet finds much of the error. The effectiveness of TAPER depends on several properties of the alignment (e.g. evolutionary divergence levels) and the errors (e.g. their length). By enabling data clean up with minimal loss of signal, TAPER can improve downstream analyses such as phylogenetic reconstruction and selection detection. Data errors, small or large, can reduce confidence in the downstream results, and thus, eliminating them can be beneficial even when downstream analyses are not impacted.
- Award ID(s):
- 1845967
- NSF-PAR ID:
- 10448314
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 11
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 2145-2158
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Results We propose SubseqHash, a strategy that uses subsequences, rather than substrings, as seeds. Formally, SubseqHash maps a string of length n to its smallest subsequence of length k, k < n, according to a given order overall length-k strings. Finding the smallest subsequence of a string by enumeration is impractical as the number of subsequences grows exponentially. To overcome this barrier, we propose a novel algorithmic framework that consists of a specifically designed order (termed ABC order) and an algorithm that computes the minimized subsequence under an ABC order in polynomial time. We first show that the ABC order exhibits the desired property and the probability of hash collision using the ABC order is close to the Jaccard index. We then show that SubseqHash overwhelmingly outperforms the substring-based seeding methods in producing high-quality seed-matches for three critical applications: read mapping, sequence alignment, and overlap detection. SubseqHash presents a major algorithmic breakthrough for tackling the high error rates and we expect it to be widely adapted for long-reads analysis.
Availability and implementation SubseqHash is freely available at https://github.com/Shao-Group/subseqhash.
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Abstract The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated.
We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling.
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