A model-based approach to study ant energetics from trajectory data
Clicks: 33
ID: 315143
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
9.6
/100
33 views
12 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #11 of 101 articles by views in PNAS nexus
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Whether an insect belongs to a solitary or a social species, its survival depends on how it consumes available energy resources. The characterization of energy consumption has largely relied on flow respirometry, a technique that is difficult to implement on individuals in isolation and is ill-conceived to disaggregate energy costs among individuals in a colony. Toward addressing these issues, we introduce an integrative framework that combines the growing area of video tracking with rigorous biomechanical modeling to infer locomotion energetics in ants. Our approach rests upon a novel physics-based model for hexapedal locomotion in the sagittal plane, which predicts forward motion and vertical oscillations in terms of few model parameters that can be retrieved from the literature or mathematically constrained. We show that individual ants favor adaptability over energy efficiency by incorporating higher step frequencies and rely less on energy recovery. A mechanistic approach to input energy estimation, combined with biomechanical analysis, provides a valuable toolkit for biologists to investigate ant energetics and perform informative inter-species comparison, from trajectory data.
| Reference Key |
openalex_W7162692052
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Basit Yaqoob, Michael Napoli, Nicola Pugno, Maurizio Porfiri |
| Journal | PNAS nexus |
| Year | 2026 |
| DOI |
10.1093/pnasnexus/pgag174
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.