Reconstructing Long-Term Changes in Avian Populations Using Lake Sediments: Opening a Window Onto the Past
Bibliographic record
Abstract
The lack of long-term monitoring data for many wildlife populations is a limiting factor in establishing meaningful and achievable conservation goals. Even for well-monitored species, time series are often very short relative to the timescales required to understand a population’s baseline conditions before the contemporary period of increased human impacts. To fill in this critical information gap, techniques have been developed to use sedimentary archives to provide insights into long-term population dynamics over timescales of decades to millennia. Lake and pond sediments receiving animal inputs (e.g., feces, feathers) typically preserve a record of ecological and environmental information that reflects past changes in population size and dynamics. With a focus on bird-related studies, we review the development and use of several paleolimnological proxies to reconstruct past colony sizes, including trace metals, isotopes, lipid biomolecules, diatoms, pollen and non-pollen palynomorphs, invertebrate sub-fossils, pigments, and others. We summarize how animal-influenced sediments, cored from around the world, have been successfully used in addressing some of the most challenging questions in conservation biology, namely: How dynamic are populations on long-term timescales? How may populations respond to climate change? How have populations responded to human intrusion? Finally, we conclude with an assessment of the current state of the field, challenges to overcome, and future potential for research.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".