Geographically well-distributed citizen science data reveals range-wide variation in the chipping sparrow's simple song

Searfoss, Abigail M.; Liu, Wanchun; Creanza, Nicole

doi:10.1016/j.anbehav.2019.12.012

Cited by 24 publications

(39 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, local variability of a species-specific call can change over time (Adret-Hausberger, 1986;Ince et al, 1980) or stay consistent without measurable temporal variation (Fournet et al, 2018;Whitney, 1992). There is also evidence that vocalization differences can be predicted by geographic distances (Marova et al, 2010;Röhr et al, 2020;Searfoss et al, 2020) or barriers (Jang et al, 2011;Zuk et al, 2001), but we do not know whether geographic isolation is a driver or product of different vocalizations.…”

Section: Introductionmentioning

confidence: 88%

“…Recently, however, the rise of citizen science projects has enabled data collection on a much greater temporal and spatial scale than ever before (Aceves‐Bueno et al, 2017; Callaghan et al., 2019; Lukyanenko et al., 2016; McKinley et al., 2017; Silvertown, 2009). Such datasets facilitate a breadth of ecological studies that would not be feasible otherwise (Diblíková et al., 2019; Mitchell et al., 2020; Searfoss et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anuran accents: Continental‐scale citizen science data reveal spatial and temporal patterns of call variability

Weaver

Callaghan

Rowley

2020

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Anuran accents: Continental‐scale citizen science data reveal spatial and temporal patterns of call variability

Weaver

Callaghan

Rowley

2020

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…In testing Chipper, we found that it produced robust estimates of sound properties for a set of synthetic recordings, and these results were consistent within and between users and in the presence of natural and white noise (Supporting Information). We hope Chipper, in tandem with citizen‐science data, can aid in large‐scale spatiotemporal studies of acoustic signals, particularly global inter‐ and intra‐species studies of birdsong (Searfoss, Liu, & Creanza, ). Chipper's song measurements could also prove valuable in studying the complex temporal variations associated with duets or coordinated singing.…”

Section: Discussionmentioning

confidence: 99%

Chipper: Open‐source software for semi‐automated segmentation and analysis of birdsong and other natural sounds

2020

Self Cite

View full text Add to dashboard Cite

Audio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high‐throughput methods of analysis for researchers. Specifically, there is a need in the community for open‐source methods that are tailored to recordings of varying qualities and from multiple species collected in nature. We developed Chipper, a Python‐based software to semi‐automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining song syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper's accuracy, repeatability and reproducibility. Chipper provides an effective way to quickly generate quantitative, reproducible measures of birdsong. The cross‐platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analysing field recordings. Chipper streamlines the processing of audio recordings with multiple user‐friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen‐science data and increases the feasibility of large‐scale multi‐species birdsong studies.

show abstract

“…Here we introduce spatzie, an R/Bioconductor package named after the German diminutive for sparrow and inspired by the long-range geographical patterns of their songs [7], a reference to the long-range genomic interactions of transcription factor cooperativity. Within spatzie we implement a collection of statistical methods to identify transcription factor co-enrichment in experimental data obtained by proteincentric chromatin conformation methods such as ChIA-PET [8] and HiChIP [9].…”

Section: Introductionmentioning

confidence: 99%

spatzie: An R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions

Hammelman

Krismer

Gifford

2021

Preprint

View full text Add to dashboard Cite

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

show abstract

Geographically well-distributed citizen science data reveals range-wide variation in the chipping sparrow's simple song

Cited by 24 publications

References 96 publications

Anuran accents: Continental‐scale citizen science data reveal spatial and temporal patterns of call variability

Anuran accents: Continental‐scale citizen science data reveal spatial and temporal patterns of call variability

Chipper: Open‐source software for semi‐automated segmentation and analysis of birdsong and other natural sounds

spatzie: An R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions

Contact Info

Product

Resources

About