On the Perils of Ignoring Evolution in Networks
Bibliographic record
Abstract
Here, we reply to the stimulating comments from Sagoff [ 1. Sagoff M. Ecological networks: response to Segar et al. Trends Ecol. Evol. 2020; 35: 862-863 Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar ] and Rossberg [ 2. Rossberg A.G. What are the fundamental questions regarding evolution in ecological networks?. Trends Ecol. Evol. 2020; 35: 863-865 Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar ] on Segar et al. [ 3. Segar S.T. et al. The role of evolution in shaping ecological networks. Trends Ecol. Evol. 2020; 35: 454-466 Abstract Full Text Full Text PDF PubMed Scopus (33) Google Scholar ]. Sagoff posits that species assemblages are largely fortuitous and ephemeral, which thwarts opportunities for coevolutionary processes [ 4. Janzen D.H. On ecological fitting. Oikos. 1985; 45: 308-310 Crossref Google Scholar ]. Given the dynamic nature of ecological communities, have populations from different interacting species had sufficient time in which to generate selective pressure on each other? As Rossberg points out, in long-lasting and highly intimate bipartite networks, ‘frequent co-occurrence of the two taxa’ is required for evolutionary lockstep between vulnerability (v) and foraging (f) traits. Fitness ‘seascapes’ [ 2. Rossberg A.G. What are the fundamental questions regarding evolution in ecological networks?. Trends Ecol. Evol. 2020; 35: 863-865 Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar ] stem from constant community turnover: but the adaptive troughs and peaks of the shifting seascape can persist and allow reciprocal evolutionary change if allelic turnover is rapid and selection strong enough. How do we specify ‘frequent co-occurrence’? Since Janzen’s 1985 appraisal of coevolution [ 4. Janzen D.H. On ecological fitting. Oikos. 1985; 45: 308-310 Crossref Google Scholar ], Colpoda protozoans have been through over 53 000 generations: resistance to mosquito predators develops in 50 [ 5. terHorst C.P. et al. Evolution of prey in ecological time reduces the effect size of predators in experimental microcosms. Ecology. 2010; 91: 629-636 Crossref PubMed Scopus (54) Google Scholar ]. We do agree that ecological (nongenetic) fitting is widespread. However, biotic selection within ecological networks does occur, is detectable, and its effects are far from trivial. Ecological Networks: Response to Segar et al.Mark SagoffTrends in Ecology & EvolutionMay 11, 2020In BriefSegar at al. [1] have proposed, ‘The structure of ecological networks reflects the evolutionary history of their biotic components.’ Janzen [2] argued that a ‘major part of the earth's surface may be occupied largely by organisms that are rich in ecological interactions and have virtually no detailed evolutionary history with one another.’ If an ecological network includes these adventitious kinds of organisms and interactions, there is no shared evolutionary history for its structure to reflect. Full-Text PDF What Are the Fundamental Questions Regarding Evolution in Ecological Networks?Axel G. RossbergTrends in Ecology & EvolutionJuly 13, 2020In BriefReviewing ‘The Role of Evolution in Shaping Ecological Networks’ by Segar et al. [1] stirred controversy over the central question [2]. I propose to settle this by defining concepts more clearly, answer other outstanding questions raised by Segar et al., and draw attention to some very different related open fundamental questions. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".