Published September 2021
| Version v1
Journal article
Open
Linking ecomechanical models and functional traits to understand phenotypic diversity
Creators
- Higham, Timothy E.1
- Ferry, Lara A.2
- Schmitz, Lars3
- Irschick, Duncan J.4
- Starko, Samuel5
- Anderson, Philip S.L.6
- Bergmann, Philip J.7
- Jamniczky, Heather A.8
- Monteiro, Leandro R.9
- Messier, Julie10
- Carrington, Emily11
- Farina, Stacy C.12
- Feilich, Kara L.13
- Hernandez, L. Patricia14
- Johnson, Michele A.15
- Kawano, Sandy M.14
- Law, Chris J.11
- Longo, Sarah J.16
- Martin, Christopher H.17
- Martone, Patrick T.5
- Rico-Guevara, Alejandro11
- Santana, Sharlene E.11
- Niklas, Karl J.18
- 1. University of California, Riverside
- 2. Arizona State University
- 3. Claremont McKenna, Pitzer, and Scripps Colleges
- 4. University of Massachusetts Amherst
- 5. University of British Columbia
- 6. University of Illinois at Urbana-Champaign
- 7. Clark University
- 8. University of Calgary
- 9. Universidade Estadual do Norte Fluminense
- 10. University of Waterloo
- 11. University of Washington
- 12. Howard University
- 13. University of Chicago
- 14. George Washington University
- 15. Trinity University
- 16. Towson University
- 17. University of California, Berkeley
- 18. Cornell University
Description
Physical principles and laws determine the set of possible organismal phenotypes. Constraints arising from development, the environment, and evolutionary history then yield workable, integrated phenotypes. We propose a theoretical and practical framework that considers the role of changing environments. This 'ecomechanical approach' integrates functional organismal traits with the ecological variables. This approach informs our ability to predict species shifts in survival and distribution and provides critical insights into phenotypic diversity. We outline how to use the ecomechanical paradigm using drag-induced bending in trees as an example. Our approach can be incorporated into existing research and help build interdisciplinary bridges. Finally, we identify key factors needed for mass data collection, analysis, and the dissemination of models relevant to this framework.
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Additional details
Identifiers
- DOI
- 10.1016/j.tree.2021.05.009
- Other
- oai:uchicago.tind.io:14123
Funding
- National Science Foundation
- IOS 1839786