Orlin S Todorov

@orlinst.bsky.social

Biostatistician, TIA, UTAS Evolution, Brains, Cognition, Stats, R

Unsolicited listicle: My list of the most criminally underused/underappreciated phylogenetic comparative methods. Note, I am not involved in ANY of these methods; but I see them as things people are often asking of comparative data but have been surprised at how infrequently they have been cited.

Extinct Pleistocene carnivores were diurnal and highly active - fresh off the press 🐯 Only BMR and diurnality are robust predictors of extinction and this also stands for extant species 🦨🐅🦝 With @johnalroy.bsky.social we use an exhaustive sample and account for phylogenetic and trait uncertainty.

https://nsojournals.onlinelibrary.wiley.com/doi/10.1002/ecog.08061https://nsojournals.onlinelibrary.wiley.com/doi/10.1002/ecog.08061

Extinct Pleistocene carnivores were diurnal and highly active - fresh off the press 🐯 Only BMR and diurnality are robust predictors of extinction and this also stands for extant species 🦨🐅🦝 With @johnalroy.bsky.social we use an exhaustive sample and account for phylogenetic and trait uncertainty.

https://nsojournals.onlinelibrary.wiley.com/doi/10.1002/ecog.08061https://nsojournals.onlinelibrary.wiley.com/doi/10.1002/ecog.08061

1/ A longtime Wired editor just wrote a mush-brained essay about how he totally missed the political rot of Silicon Valley (& still doesn't get it). But in the late 1990s, a Wired journalist warned of a toxic ideology bubbling up from tech. Paulina Borsook has largely been erased. Let's change that

photo of paulina borsook

Public info institution the BoM never mentions fossil-fuelled global heating, even during mega-droughts, heatwaves, bushfires floods. Why? Perhaps because its board is controlled by Shell, Santos, Woodside & Chevron? If you think this stinks, make a subsmission! michaelwest.com.au/undue-influe...

Undue Influence: oil and gas giants infiltrate Australia's Bureau of Meteorology - Michael West

Oil giants Shell, Santos, Woodside and Chevron finance the Bureau of Meteorology. The BOM does not discuss "climate change".

michaelwest.com.au

We are recruiting a PhD student to work on a decision-support tool for food producers to assess and optimise the environmental sustainability of products. Ideally, someone who is knowledgeable about food production, sustainability and can or is willing to learn how to code in R/Shiny or Python.

Available projects for research degrees | University of Tasmania - Environmentally sustainable food product

Globally, there is an urgent need to make food production environmenta...

utas.edu.au

🚨 First PhD chapter is out! My work thus far, with @andy2dobson.bsky.social We found that formerly common species have declined the fastest, on average. 📄 North American bird declines are driven by reductions in common species | Science Advances www.science.org/doi/10.1126/...

North American bird declines are driven by reductions in common species

Declines in North American birds are driven not by rare species vanishing but by sharp losses among formerly common species.

science.org

Missing data and the problem of handling variable-incomplete datasets is a common feature of ecological, evolutionary, biogeographical and palaeontological analyses. Here is a refreshing review arguing for the application of a generalized "Multiple Imputation" Rubin (1976) procedure 👇

Francisco Rodriguez-Sanchez@frodsan.bsky.social · last yr.

The fallacy of single imputation for #traits databases: Use multiple imputation instead @methodsinecoevol.bsky.social #ecopubs doi.org/10.1111/2041...


    The past few years have seen the publication of many new trait databases. A common problem with large databases is a lack of completeness, or inversely, the high prevalence of missing values.
    Biologists have developed several methods to impute (fill in) missing values. This allows ordinary statistical procedures to be used in analyses and the use of only complete cases, with a concomitant loss of power and accuracy, can be avoided.
    Often, biologists use simulation to test new methods by deleting values from a dataset and recording how well the imputed values match the known, removed values.
    Here we argue that this is a poor measure of the strength of an imputation method. We also describe the importance and logic of the statistical procedure of multiple imputation, which requires that the imputations need not be precise or accurate estimates of the missing data.