Designing eco‐evolutionary experiments for restoration projects: Opportunities and constraints revealed during stickleback introductions
Bibliographic record
Abstract
Abstract Eco‐evolutionary experiments are typically conducted in semi‐unnatural controlled settings, such as mesocosms; yet inferences about how evolution and ecology interact in the real world would surely benefit from experiments in natural uncontrolled settings. Opportunities for such experiments are rare but do arise in the context of restoration ecology—where different “types” of a given species can be introduced into different “replicate” locations. Designing such experiments requires wrestling with consequential questions. (Q1) Which specific “types” of a focal species should be introduced to the restoration location? (Q2) How many sources of each type should be used—and should they be mixed together? (Q3) Which specific source populations should be used? (Q4) Which type(s) or population(s) should be introduced into which restoration sites? We recently grappled with these questions when designing an eco‐evolutionary experiment with threespine stickleback ( Gasterosteus aculeatus ) introduced into nine small lakes and ponds on the Kenai Peninsula in Alaska that required restoration. After considering the options at length, we decided to use benthic versus limnetic ecotypes (Q1) to create a mixed group of colonists from four source populations of each ecotype (Q2), where ecotypes were identified based on trophic morphology (Q3), and were then introduced into nine restoration lakes scaled by lake size (Q4). We hope that outlining the alternatives and resulting choices will make the rationales clear for future studies leveraging our experiment, while also proving useful for investigators considering similar experiments in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".