
Python port and Iberian replication of Soroye et al. 2020 Bumble bee climate exposure → local extinction Tests whether the thermal-exposure mechanism reported by Soroye, Newbold and Kerr (2020, *Science*) — an increasing frequency of temperatures exceeding species-specific historical tolerances predicts local bumble bee extinction — replicates on the Iberian Peninsula using an independent Python re-implementation of the authors' R pipeline, GBIF occurrence data, and CRU TS 3.24.01 climate. Headline result. The claim replicates: the Iberian coefficient (sc_TEI_delta = +0.48, 95 % CI [0.27, 0.69]) is positive and significant, and larger in magnitude than the continental mean Soroye reports (+0.15 in our Python-port validation on his own data). Reference paper: Soroye P., Newbold T., Kerr J. (2020). Climate change contributes to widespread declines among bumble bees across continents. *Science* 367(6478):685–688. DOI: [10.1126/science.aax8591](https://doi.org/10.1126/science.aax8591) What's in this release - `soroye_port/` — five-script Python port of Soroye's R pipeline: occurrence cleaning, 100 km equal-area presence–absence grids, sampling-effort rasters, TEI/PEI climate indices, and the mixed-effects logistic GLMM. - Two entry points: `01_clean_data.py` (Soroye's NA + Europe data, Phase 2 port validation) and `01_clean_data_iberia.py` (GBIF Iberia, Phase 3 regional replication). Scripts 02–05 are shared. - Dockerfile, Snakefile, continuous-integration smoke test, and a Jupyter Book that narrates the FORRT reproduction + replication story. What changed from v0.1.0 Replaces the earlier simplified monthly-means analysis with a line-by-line port of Soroye's five R scripts. The preliminary v0.1.0 coefficient sign was a methodology artefact and is superseded by the values above. Source DOI on Zenodo: 10.5281/zenodo.19701133 Docker image DOI on Zenodo: minted from this release (concept DOI will be linked from the README)
If you use this software, please cite both this repository and the original paper by Decrop et al. (2025).
reproduction, marine biodiversity, phytoplankton, deep learning, reproducibility, EfficientNetV2, FlowCam, FAIR, FORRT
reproduction, marine biodiversity, phytoplankton, deep learning, reproducibility, EfficientNetV2, FlowCam, FAIR, FORRT
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