Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

Macro evolutionary patterns do not predict micro evolutionary trajectories

Created on 13 Jul 2026

Authors

James, C. C., Goncalves Leles, S., Buck-Wiese, H., Landry, Z. C., Morris, E., Marshall, D., Levine, N. M.

Abstract

As the world's oceans change in response to climate change, phytoplankton communities will adapt to warmer, more stratified surface waters via plasticity, evolution, and range shifts. Current global ocean models assume that size structured phytoplankton communities have fixed trait relationships, and as a result generally predict that smaller size classes will become more dominant globally. However, this general expectation fails to consider how intra-species trait tradeoffs may operate orthogonally from large-scale inter-species tradeoffs--allowing for alternative evolutionary pathways given the limits and/or possibilities available to ancestral populations. To identify evolutionary pathways phytoplankton populations might take, we develop a novel modeling framework that combines a trait-based phytoplankton quota model with stochastic evolution (ecoTRACE). EcoTRACE explicitly decouples key phytoplankton traits from interspecific allometric relationships, allowing for novel phenotypes to emerge. We validated ecoTRACE against a long-term artificial size selection experiment on Dunaliella tertiolecta. We show that ecoTRACE captures multi-dimensional evolved phenotypes that quota models based on interspecific relationships fail to reproduce. Under fluctuating multi-stressor growth, model populations evolve phenotypic plasticity that deviates from predicted interspecific allometric relationships. EcoTRACE provides a framework for generating hypotheses as to the evolutionary trajectories that phytoplankton will experience in a warmer, more variable ocean.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 13 Jul 2026.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 11
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement