{"id":"W4406343439","doi":"10.1111/ecog.07546","title":"Integrating food webs in species distribution models can improve ecological niche estimation and predictions","year":2025,"lang":"en","type":"article","venue":"Ecography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"HORIZON EUROPE Framework Programme; Agence Nationale de la Recherche; European Commission; Biodiversa+","keywords":"Niche; Ecological niche; Adaptability; Ecology; Trophic level; Computer science; Flexibility (engineering); Species distribution; Environmental niche modelling; Ecological network; Ecosystem; Biology; Mathematics; Habitat; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001297656,0.00009876833,0.0001002298,0.00005901869,0.000151875,0.00005115255,0.0000789743,0.00007587692,0.001434727],"category_scores_gemma":[0.00005528325,0.00008911457,0.00004886954,0.0005536912,0.0001625057,0.000155406,0.00009726661,0.0001319264,0.00001187812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677795,"about_ca_system_score_gemma":0.000006363774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007216316,"about_ca_topic_score_gemma":0.003764074,"domain_scores_codex":[0.9992728,0.00003030522,0.0001788174,0.0002326676,0.00009665947,0.0001887636],"domain_scores_gemma":[0.9997581,0.0000412433,0.00004253007,0.0001031928,0.000008204683,0.00004671717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003664845,0.0005489631,0.8786218,0.00003727925,0.00004210214,0.000003005426,0.0006292938,0.002113796,0.001246453,0.09096139,0.01543184,0.01032742],"study_design_scores_gemma":[0.0003749518,0.0001157544,0.9478129,0.00001645825,0.00001502336,0.000001267206,0.001573215,0.038374,0.0003352491,0.008153411,0.003096473,0.0001313607],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640645,0.00004812547,0.006867853,0.0008894764,0.0001463677,0.0002244394,0.0003660909,0.00006438908,0.02732874],"genre_scores_gemma":[0.9992715,0.00006720828,0.0001500573,0.0001131857,0.000007922019,0.00006452696,0.0002199868,0.000002715533,0.0001029627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08280797,"threshold_uncertainty_score":0.9994781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531393666857687,"score_gpt":0.2256550778792973,"score_spread":0.2103411412107205,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}