{"id":"W2984967191","doi":"10.1111/geb.13029","title":"Species niches, not traits, determine abundance and occupancy patterns: A multi‐site synthesis","year":2019,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Agence Nationale de la Recherche; Fundação de Amparo à Pesquisa do Estado de São Paulo; Fondation pour la Recherche sur la Biodiversite; International Institute of Tropical Forestry; National Science Foundation; Royal Society of Edinburgh; Royal Society; Universidad Nacional de Rosario; Carnegie Trust for the Universities of Scotland; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Niche; Ecological niche; Ecology; Occupancy; Biology; Abundance (ecology); Trait; Niche segregation; Habitat; Range (aeronautics); Niche differentiation; Taxon; Optimal distinctiveness theory; Environmental niche modelling","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005215787,0.0007320693,0.00161313,0.00236891,0.0005919952,0.002157586,0.0008751521,0.0005618875,0.007576227],"category_scores_gemma":[0.009723787,0.0005010443,0.00357337,0.002151937,0.001057117,0.001515783,0.00153371,0.0006300468,0.0004610177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006436519,"about_ca_system_score_gemma":0.0006686921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005572147,"about_ca_topic_score_gemma":0.007267865,"domain_scores_codex":[0.996896,0.001593359,0.0002931851,0.0009066224,0.0002188024,0.0000919756],"domain_scores_gemma":[0.9795077,0.01508549,0.001527157,0.002236956,0.001330822,0.0003118645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.0005608116,0.0002326265,0.8277793,0.007527616,0.02975931,0.000545965,0.001822667,0.0119831,0.01086674,0.007866092,0.001328696,0.09972709],"study_design_scores_gemma":[0.0000687801,0.0007114009,0.9163351,0.00145489,0.01935635,0.0004677359,0.002015451,0.03379694,0.002273072,0.01501234,0.008360059,0.0001478924],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8773995,0.03411418,0.07731788,0.0005950648,0.0002178691,0.000232225,0.004627527,0.0002792031,0.005216454],"genre_scores_gemma":[0.9897466,0.001560258,0.00744981,0.00008004669,0.00005126946,0.00006734143,0.00055874,0.00004179801,0.0004441194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007576227,"threshold_uncertainty_score":0.02758408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130171468140917,"score_gpt":0.2176904279106571,"score_spread":0.2046732810965654,"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."}}