FN Archimer Export Format PT J TI Global patterns and predictors of trophic position, body size and jaw size in fishes BT AF Kopf, R. Keller Yen, Jian D. L. Nimmo, Dale G. Brosse, Sébastien Villeger, Sébastien Tittensor, Derek AS 1:1,2;2:3;3:2;4:4;5:5;6:; FF 1:;2:;3:;4:;5:;6:; C1 Research Institute for the Environment and Livelihoods Charles Darwin University Darwin Northern Territory ,Australia Institute for Land Water & Society Charles Sturt University Albury New South Wales, Australia School of BioSciences The University of Melbourne Parkville Victoria ,Australia Laboratoire Évolution & Diversité Biologique (EDB UMR5174) Université Paul Sabatier ‐ Toulouse 3, CNRS, IRD, UPS Toulouse Cedex ,France MARBEC, University Montpellier, CNRS, IFREMER, IRD Montpellier ,France C2 UNIV CHARLES DARWIN, AUSTRALIA UNIV CHARLES STURT, AUSTRALIA UNIV MELBOURNE, AUSTRALIA UNIV TOULOUSE, FRANCE UNIV MONTPELLIER, FRANCE UM MARBEC IN WOS Cotutelle UMR copubli-france copubli-univ-france copubli-int-hors-europe IF 6.909 TC 7 UR https://archimer.ifremer.fr/doc/00661/77349/78823.pdf https://archimer.ifremer.fr/doc/00661/77349/78824.pdf https://archimer.ifremer.fr/doc/00661/77349/78825.tif https://archimer.ifremer.fr/doc/00661/77349/78826.tif https://archimer.ifremer.fr/doc/00661/77349/78827.tif https://archimer.ifremer.fr/doc/00661/77349/78828.tif https://archimer.ifremer.fr/doc/00661/77349/78829.tif https://archimer.ifremer.fr/doc/00661/77349/78830.docx LA English DT Article DE ;allometric trophic network models;allometry;body mass;gape limitation;machine learning;predator–prey;random forest;trophic network theory AB Aim The aim of this study was test whether maximum body mass and jaw length are reliable predictors of trophic position (TP) in fishes, and to compare linear and nonlinear machine‐learning (ML) models incorporating biogeography, habitat and other morphological traits. Location Global. Time period Modern. Major taxa studied Fishes. Methods We compiled a global database of TP (2.0–4.5), maximum body mass, jaw length, order, ecoregion, habitat and other morphological traits of freshwater, estuarine and diadromous fishes (n = 1,991). We used Bayesian linear mixed effects and ML, with r2 analogues and 10‐fold cross‐validation, to explain and predict TP. Results Random forest models outperformed Bayesian models in all comparisons. Jaw length was the most influential predictor of TP, but was weakly associated with body mass except in five orders of largely piscivorous fishes. Trophic position did not scale positively with body mass in global ecoregions, riverine fishes, or in 29/30 orders, but scaled positively in lacustrine fishes and Perciformes. Significant negative TP–body mass scaling was observed in Characiformes. Best models explained 55% of the global variation in TP, but over‐estimated the position of herbivores‐detritivores, and under‐estimated the position of top predators. Main conclusions Our study provides support for jaw length as an important mechanism constraining TP in one of the world’s largest groups of vertebrates. Jaw length and body mass were weakly correlated, and therefore body size was not a strong predictor of TP. The diversification of large‐bodied herbivores‐detritivores and omnivores in freshwater ecosystems, coupled with small predators in species‐rich orders (e.g., Cypriniformes, Characiformes) in temperate and tropical rivers explains why TP globally shows a weak relationship with body size. Our model validation results underscore the importance of not assuming that explanatory power extends to predictive capacity in macroecology and machine‐learning models. PY 2021 PD FEB SO Global Ecology And Biogeography SN 1466-822X PU Wiley VL 30 IS 2 UT 000591648500001 BP 414 EP 428 DI 10.1111/geb.13227 ID 77349 ER EF