Defining and classifying migratory habitats as sources and sinks: The migratory pathway approach
Bibliographic record
Abstract
Abstract Understanding and conserving migratory species requires a method for characterizing the seasonal flow of animals among habitats. Source‐sink theory describes the metapopulation dynamics of species by classifying habitats as population sources (i.e. net contributors) or sinks (i.e. net substractors). Migratory species may have non‐breeding habitats important to the species (e.g. overwintering or stopover habitats) that traditional source‐sink theory would classify as sinks because these habitats produce no individuals. Conversely, existing migratory network models can evaluate the relative contribution of non‐breeding nodes, but these models make an equilibrium assumption that is difficult to meet when examining real migratory populations. We extend a pathway‐based metric allowing breeding habitats, non‐breeding habitats and migratory pathways connecting these habitats to be classified as sources or sinks. Rather than being based on whether place‐ or season‐specific births exceed deaths, our approach quantifies the total demographic contribution from a node or migratory pathway over a flexibly defined yet limited time period across an organism's life cycle. As such, it provides a snapshot of a migratory system and therefore does not require assumptions associated with equilibrium dynamics. We first develop a generalizable mathematical notation and then demonstrate how the metric may be used with two case studies: the common loon ( Gavia immer ) and Yellowstone cutthroat trout ( Oncorhynchus clarkii bouvieri ). These examples highlight how stressors can impact stopover and wintering habitats (loons) and habitat management targeting migratory pathways can improve population status (trout). Synthesis and applications . Each of the two case studies presented describes how effects at one location are felt by populations in another through the seasonal flow of individuals. The contribution metric we present should be helpful in allocating regulatory and management attention to times and locations most critical to migratory species persistence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".