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Pixelisation. Soroye 2020 fit at the CEA grid (~100 km, equal-area cylindrical projection at the equator). This Study additionally fits at HEALPix-NESTED nside=64 (~92 km cells on the WGS84 ellipsoid). Cell area is comparable (within ~10%); cell shape and topology differ.\n\n2. Region. Soroye 2020 fit on North America + whole Europe (all Western Palearctic Bombus). This Study fits on Iberian peninsula only. Iberia is a subset of Soroye\'s European fitting domain; the same GLMM specification applies.\n\n3. Inference engine. Soroye 2020 used MCMCglmm (R). This Study uses two independent Python implementations (statsmodels variational-Bayes for the fast first pass; bambi/PyMC NUTS for the authoritative posterior HDIs reported in the Outcome). Both target the same Bayesian posterior.\n\n4. Prior choices. Soroye 2020\'s MCMCglmm priors (default informative inverse-Wishart on variance components) are not exactly reproducible across to bambi (Half-StudentT default on group SDs). This is the largest non-trivial methodological deviation; it should not affect the headline coefficient sign or order of magnitude (verified: it does not). The full prior table is in notebooks/03h_analysis_healpix.py.\n\n5. Climate inputs. Soroye 2020\'s CRU TS 3.24.01 from his Figshare deposit is used unchanged \342\200\224 NOT a deviation, this is the SAME climate dataset.\n\n6. Sampling-effort proxy. Same as Soroye (cells_per_decade \303\227 records_per_cell from the cleaned occurrence table) \342\200\224 NOT a deviation.\n" } } } rows { name { value: "hasDiscipline" } } rows { name { value: "Q641498" } } rows { quad { p_iri { } o_iri { prefix_id: 12 } } } rows { name { value: "hasMethodologyDescription" } } rows { quad { p_iri { prefix_id: 9 } o_literal { lex: "METHOD\n\nTier 1 \342\200\224 historical fit.\n\n(1) Climate inputs. Soroye\'s bundled CRU TS 3.24.01 NetCDFs (mean monthly temperature and total monthly precipitation, 1901\342\200\2232014, 0.5\302\260 lat-lon grid) are downloaded from Soroye\'s Figshare deposit and used unchanged. No substitution with cdsapi or other reanalysis sources at fit time \342\200\224 by design, to keep the climate forcing identical to the original.\n\n(2) Occurrence inputs. GBIF Iberian Bombus occurrences are issued via own-DOI download (cited in the Outcome step 05 \"evidence\" field). Filtering follows Soroye\'s species list (genus Bombus, valid taxonomy, occurrences geo-referenced to the Iberian peninsula bounding box).\n\n(3) GLMM specification. Replicates Soroye\'s exact formula:\n extinction ~ continent + sc_sampling + sc_TEI_bs + sc_TEI_delta + sc_TEI_bs:sc_TEI_delta + sc_PEI_bs + sc_PEI_delta + sc_PEI_bs:sc_PEI_delta + sc_TEI_bs:sc_PEI_bs + sc_TEI_delta:sc_PEI_delta + (1|species)\nwhere TEI = (T_obs \342\210\222 T_min_spp) / (T_max_spp \342\210\222 T_min_spp), PEI is the analogous precipitation index, and sc_ is within-substrate z-scoring.\n\n(4) Two spatial substrates.\n Pass 1 (CEA): cell coverage and per-species niche limits computed on Soroye\'s original CEA grid (~100 km cells); GLMM fit on this grid.\n Pass 2 (HEALPix nside=64): cell coverage re-computed on HEALPix-NESTED nside=64 cells of the WGS84 ellipsoid (using the healpix-geo Python library); per-species niche limits and the GLMM are refit at this substrate.\n\n(5) Inference. Two independent fitting strategies on the same Pass-2 design matrix:\n (a) variational-Bayes mean-field via statsmodels.BinomialBayesMixedGLM (fast first pass);\n (b) full-posterior NUTS via bambi/PyMC, 4 chains \303\227 2000 samples (authoritative HDIs).\n\nTier 2 \342\200\224 SSP3-7.0 future projection \342\200\224 runs on the DestinE Jupyter platform and is part of this same chain\'s Outcome (step 05), not a separate Study. Briefly: DestinE Climate DT IFS-NEMO standard SSP3-7.0 GRIB files are retrieved via polytope on LUMI for the 2020\342\200\2232029 and 2030\342\200\2232039 horizons, decoded with eccodes (Python API, NESTED-aware), subset to pre-computed Iberian HEALPix cells, aggregated to monthly means matching the CRU TS units, and used to compute future TEI_delta and PEI_delta per species per cell. Per-species ranking is reported following the protocol established in the methodological sibling chain.\n\nCode: notebooks/01_data_download.py (CRU TS + GBIF), notebooks/02_data_clean.py (CEA Pass 1 cleaning), notebooks/02h_data_clean_healpix.py (HEALPix Pass 2 cleaning), notebooks/03_analysis.py (CEA fit), notebooks/03h_analysis_healpix.py (HEALPix fit), notebooks/04_figures.py + 04h_figures_healpix.py (figures)." } } } rows { name { value: "hasScopeDescription" } } rows { quad { p_iri { } o_literal { lex: "SCOPE: the GLMM coefficient on standardised TEI_delta (Soroye et al. 2020\'s \"climatic position index change\" between baseline 1901\342\200\2231974 and recent 2000\342\200\2232014), restricted to Bombus species observed on the Iberian peninsula in GBIF.\n\nIN SCOPE\n - Soroye 2020\'s GLMM specification: extinction ~ continent + sc_sampling + sc_TEI_bs + sc_TEI_delta + sc_PEI_bs + sc_PEI_delta + (TEI:PEI interactions) + (1|species).\n - Soroye 2020\'s CRU TS 3.24.01 climate inputs 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