How to infer population trends in sparse data: examples with opportunistic sighting records for great white sharks
Bibliographic record
Abstract
Abstract Aim Biodiversity is declining at accelerating rates. Understanding past and ongoing changes to biodiversity is paramount in prioritizing conservation action and restoring functional ecosystems. Yet long‐term, systematic data on the distribution and abundance of species are sparse. For many organisms, specimen collections and anecdotal accounts of chance sightings or captures constitute the only source of information. Such data have the potential to provide valuable insights on long‐term ecosystem changes, but are often neglected because they are difficult to analyse quantitatively. Here we review available methods and introduce a new approach. Location Historic data on sightings and captures of great white sharks in the eastern Adriatic and off eastern Canada serve to illustrate the utility of both the existing methods and the new approach. Method Unlike existing methods, the new approach focuses on estimating population trends rather than verifying extinction and explicitly addresses uncertainty over observation effort via two tiers of sensitivity analysis. It fits a series of generalized linear models that provide multiple estimates of declines under alternate scenarios regarding the appropriate reference period and observer trends. Programming code to implement the approach in freely available software is provided as supplementary material. Results Example analyses of great white shark sightings suggest that local populations of this species have suffered dramatic declines, both in the eastern Adriatic and along Canada’s eastern coast. Although not yet extinct, this top predator may therefore no longer be able to fulfil its former ecological role. Main conclusions Careful quantitative analyses of imperfect historical data can provide valuable insights into past ecological changes. Such insights are crucial to improved management and restoration of individual species and their ecosystems. We therefore hope that our review of available methods will facilitate quantitative evaluations of species for which analysis was previously impeded by a lack of systematically collected data.
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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 | 0.031 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".