Natural History Museum Bern, Bernastrasse 15, Bern CH3005, Switzerland
: Received: 3 September 2016 / Approved: 5 September 2016 / Online: 5 September 2016 (11:14:59 CEST)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite:
Verde Arregoitia, L. Rethinking Omnivory in Rodents. Preprints2016, 2016090017 (doi: 10.20944/preprints201609.0017.v1).
Verde Arregoitia, L. Rethinking Omnivory in Rodents. Preprints 2016, 2016090017 (doi: 10.20944/preprints201609.0017.v1).
The diets of animals reflect their evolutionary adaptations and ecological roles. Mammals have been studied intensively, but the criteria used to define diet classes are inconsistent. Although all rodents share a conserved suite of features that form a mechanical complex for gnawing, they are very diverse ecologically. Classifications of rodent diets condense data on stomach or cheek-pouch contents, food caches, behavioural observations, or faecal analysis. The most common approach is use on trophic levels and designate species as herbivorous, carnivorous, or omnivorous. A meaningful classification should consider the ecomorphological and evolutionary implications of consuming different foods. This work describes how recent studies have defined splits within herbivorous and carnivorous taxa. Biologically-relevant categories reflect the adaptive implications in cranial, dental, mandibular, and gut morphology of consuming fibrous plant parts, nonfibrous plant parts, soft-bodied invertebrates, vertebrates, or chitinous invertebrates. With some exceptions, most rodents are generalized herbivores that consume nonfibrous plant parts (fruits, seeds, gum, nectar), and will opportunistically consume animal matter, lichens, or fungi. These generalized herbivores are by definition omnivorous, so an additional category is redundant and will introduce biases and numerical complications if it is used as either a response or a predictor variable in quantitative analyses.