Learning Bayesian Statistics

#127 Saving Sharks... with Python, Causal Inference and Aaron MacNeil


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Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia and Michael Cao.

Takeaways:

  • Sharks play a crucial role in maintaining healthy ocean ecosystems.
  • Bayesian statistics are particularly useful in data-poor environments like ecology.
  • Teaching Bayesian statistics requires a shift in mindset from traditional statistical methods.
  • The shark meat trade is significant and often overlooked.
  • Ray meat trade is as large as shark meat trade, with specific markets dominating.
  • Understanding the ecological roles of species is essential for effective conservation.
  • Causal language is important in ecological research and should be encouraged.
  • Evidence-driven decision-making is crucial in balancing human and ecological needs.
  • Expert opinions are crucial for understanding species composition in landings.
  • Trade dynamics are influenced by import preferences and species availability.
  • Bayesian modeling allows for the incorporation of various data sources and expert knowledge.
  • Field data collection is essential for validating model assumptions.
  • The complexity of trade relationships necessitates a nuanced approach to modeling.
  • Understanding the impact of management interventions on landings is critical.
  • The role of scientists in informing policy is vital for effective conservation efforts.

Chapters:

00:00 Introduction to Marine Biology and Statistics

04:33 The Role of Bayesian Statistics in Marine Research

10:09 Challenges in Teaching Bayesian Statistics

21:58 The Importance of Sharks in Ecosystems

26:35 Understanding Shark Meat Trade and Conservation

32:09 The Trade in Ray and Shark Meat

36:18 Modeling Landings and Trade

42:56 Challenges in Data Integration

44:50 Running Complex Models

51:57 Expert Elicitation and Prior Construction

55:52 Future Directions and Research

56:46 Reflections on Science and Policy

Links from the show:

  • Fisheries Lab: https://ifisheries.org/?page_id=83
  • LBS #51 Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton

Transcript

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Learning Bayesian StatisticsBy Alexandre Andorra

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