Soroye 2020 thermal-exposure → extirpation claim is robust to a Python re-implementation
The Python re-implementation of Soroye et al. 2020's pipeline applied to the authors' own dataset reproduces the headline thermal-exposure → extirpation relationship: the standardised TEI-delta coefficient is positive and highly significant in both a plain logistic regression and a mixed-effects model with species random effect. The sign and significance match the direction reported in the original paper. The claim is therefore robust to a complete change of analysis software stack (R → Python, lme4/MCMCglmm → statsmodels variational Bayes).
Mixed-effects logistic GLMM (statsmodels.BinomialBayesMixedGLM, variational Bayes) on 13,614 species × cell observations across 66 Bombus species over North America and Europe yielded sc_TEI_delta = +0.1528 with 95% credible interval [0.116, 0.189], unambiguously positive. A plain logistic regression on the same data gave sc_TEI_delta = +0.2466 (p = 4×10⁻²¹). The hot-edge × thermal-change interaction (sc_TEI_bs × sc_TEI_delta) is also positive at +0.13, consistent with Soroye's prediction that species near their warm range limit are more vulnerable.
(i) Variational Bayes posterior may underestimate credible-interval width by approximately 10–20% relative to full MCMC; tight CI claims would be more conservative under full MCMC. (ii) The exact climateLimits[[1]]/[[2]] precomputation script is not in the public Figshare release and was reconstructed from the available R helper scripts; this can shift absolute coefficient magnitudes but does not alter the qualitative direction. (iii) The 13,614-row analysis subset is determined by Soroye's filter (cells with valid baseline + recent observations) and inevitably excludes regions of low sampling effort.
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