Applying Movement Ecology to Marine Animals with Complex Life Cycles
Bibliographic record
Abstract
Marine animals with complex life cycles may move passively or actively for fertilization, dispersal, predator avoidance, resource acquisition, and migration, and over scales from micrometers to thousands of kilometers. This diversity has catalyzed idiosyncratic and unfocused research, creating unsound paradigms regarding the role of movement in ecology and evolution. The emerging movement ecology paradigm offers a framework to consolidate movement research independent of taxon, life-history stage, scale, or discipline. This review applies the framework to movement among life-history stages in marine animals with complex life cycles to consolidate marine movement research and offer insights for scientists working in aquatic and terrestrial realms. Irrespective of data collection or simulation strategy, breaking each life-history stage down into the fundamental units of movement allows each unit to be studied independently or interactively with other units. Understanding these underlying mechanisms of movement within each life-history stage can then be used to construct lifetime movement paths. These paths can allow further investigation of the relative contributions and interdependencies of steps and phases across a lifetime and how these paths influence larger research topics, such as population-level movements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".