@BirdSongLab

David Logue's lab at the University of Lethbridge. We study vocal interaction, signal repertoires, and song performance in tropical songbirds.

 Lethbridge, Alberta, Canada
david-logue.squarespace.com/researchpage
Joined July 2019
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  • Followers686
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I "made" this video (more of a podcast really) by imputing seven of my papers into Google's NotebookLM AI. It's not perfect (those graphs are a mess) but it has a strong narrative, and it's probably more accurate than most science podcasts.
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Replying to @NehaftaB
I'll DM you ...
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Replying to @NehaftaB
Hi, Nehafta. I don't have funding for a postdoc, but I would be open to hosting a postdoc that brought their own funding.
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If you’re interested in joining us, please send your CV and a cover letter detailing your interest and suitability for the position to [email protected] by November 1, 2024. Feel free to reach out with any questions! (end 🧵)
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We offer a diverse, supportive lab environment that emphasizes curiosity and discovery. Our lab is part of a vibrant animal behavior community at the University of Lethbridge, spanning ethology, behavioral ecology, and behavioral neuroscience in birds, mammals, and inverts. (5/6)
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Valuable skills include: · Acoustic analysis. · Advanced statistics (e.g., mixed models, multivariate analyses, Bayesian approaches). · Programming in R and Python. · Scientific writing. · Spanish language ability (helpful for fieldwork in Puerto Rico). (4/6)
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We are looking for students with a solid background in evolution and animal behavior, who share our passion for animal communication. (3/6)
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Our lab, led by Principal Investigator Dr. David Logue, investigates interactive vocal communication, communication networks, song type repertoires, and vocal performance in the tropical songbird, Adelaide’s warbler. (2/6)
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The Birdsong Lab at the University of Lethbridge is seeking 1-2 motivated graduate students to join us starting in Fall 2025. Funding is available. (a🧵1 /6)
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Fantastic news, and well deserved! Congratulations, Christine!
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Happening next week @uLethbridge ! Thanks for the invite, @BirdSongLab and @eyewitnessIDup !
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Replying to @LeaGrie
I want a shirt like this, but better. Needs the flaming Thrasher logo, and a better bird image.
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Replying to @LeaGrie
thrashers are the best! April and I just saw Cali thrashers in the desert near San Diego -- awesome birds!
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How have we studied female bird song? Does female song function the same way as male song? Sometimes. . . Congratulations to Cristina Barros for summarizing the literature on this topic with @UrbanBirdNerd 
authors.elsevier.com/c/1jczN…
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The Birdsong Lab made the local news! (clip starts at 4:30) globalnews.ca/lethbridge/pro…
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Great to see McElreath get the recognition he deserves. This book, and the accompanying YouTube lectures, represent a revolution in applied statistics.
Richard McElreath 🐈‍⬛@rlmcelreath16 Jul 2024
The folks at @ISBA_events tell me my book Statistical Rethinking has won the 2024 DeGroot Prize for its contributions to "statistical inference, decision theory and statistical applications". This is huge honor especially given the previous winners, who have influenced me so much
Cover of 2nd edition of Statistical Rethinking

ALT Cover of 2nd edition of Statistical Rethinking

Winners of the DeGroot Prize 2021 Nicolas Chopin and Omiros Papaspiliopoulos (2020). An Introduction to Sequential Monte Carlo. Springer. 2019 Subhashis Ghosal and Aad van der Vaart. Fundamentals of Nonparametric Bayesian Inference. Cambridge University Press. 2017 David Banks, Jesus Rios, and David Rios Insua. Adversarial Risk Analysis. CRC Press. 2015 Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. Bayesian Data Analysis, Third Edition. CRC Press. 2013 Noel Cressie and Christopher Wikle (2011). Statistics for Spatio-Temporal Data. John Wiley and Sons. Kevin P. Murphy (2012). Machine Learning: A Probabilistic Perspective. MIT Press. 2011 Jay Kadane (2011). Principles of Uncertainty. CRC Press. 2009 Giovanni Parmigiani and Lurdes Inoue (2009). Decision Theory: Principles and Approaches. John Wiley and Sons. Carl Edward Rasmussen and Christopher K. I. Williams (2006). Gaussian Processes for Machine Learning (freely availa

ALT Winners of the DeGroot Prize 2021 Nicolas Chopin and Omiros Papaspiliopoulos (2020). An Introduction to Sequential Monte Carlo. Springer. 2019 Subhashis Ghosal and Aad van der Vaart. Fundamentals of Nonparametric Bayesian Inference. Cambridge University Press. 2017 David Banks, Jesus Rios, and David Rios Insua. Adversarial Risk Analysis. CRC Press. 2015 Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. Bayesian Data Analysis, Third Edition. CRC Press. 2013 Noel Cressie and Christopher Wikle (2011). Statistics for Spatio-Temporal Data. John Wiley and Sons. Kevin P. Murphy (2012). Machine Learning: A Probabilistic Perspective. MIT Press. 2011 Jay Kadane (2011). Principles of Uncertainty. CRC Press. 2009 Giovanni Parmigiani and Lurdes Inoue (2009). Decision Theory: Principles and Approaches. John Wiley and Sons. Carl Edward Rasmussen and Christopher K. I. Williams (2006). Gaussian Processes for Machine Learning (freely availa

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Una oportunidad para financiar servicios de traducción profesionales para autores latinoamericanos que tengan manuscritos casi listos y que quieran traducirlos o revisar el inglés: nabci-us.org/stork-grants-es…
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Fellow Canadian birdsong researcher, Dan Mennill, spoke about his pioneering experimental study of cultural evolution during The Golden Age of Animal Communication Networks symposium at ABS last week. #ABS2024
Dan Mennill@DMennill4 Jul 2024
How to handle in-person vs online conferences? The @AnimBehSociety has a cool approach. The in-person conference was last week. Now we upload videos of talks and posters, accessible to in-person & virtual-only attendees (with virtual office hours & online questions). #ABS2024
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Replying to @DMennill
Great course! Our lab took it a couple years back, and now we're implementing what we learned. We recently used Rraven, WarbleR, and SeeWave to extract acoustic measurements from ~12,000 songs, saving hundreds of hours of acoustic measurements.
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Great work! Our lab is also looking at song type rarity, and we're finding strong patterns too. Will you be at #ABS2024?
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