Testimonials to reconstruct past abundances of wildlife populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Long-term monitoring of wildlife populations has greatly contributed to our current understanding of population dynamics and ecosystem functioning. Despite tireless field campaigns, however, only a fraction of the biodiversity has been monitored to date and the dynamics of potential key species have yet to be understood. Here, we propose a method based on testimonials of observations from field workers to reconstruct past abundances of unmonitored populations and fill data gaps. We contacted scientists who conducted field work at the Bylot Island field station, Nunavut, in the Canadian Arctic between 1991 and 2019 and collected 205 testimonials of past observations from 131 participants. We scored each testimonial based on its content and derived annual abundance indices for three highly fluctuating taxa, being lemmings, snowy owls and ermines. These indices were compared to standardized abundance estimates based on field sampling that were either available between 1993 and 2019 (lemmings and snowy owls) or 2007–2019 (ermines). Our results show that abundance indices based on testimonials correlate well with those from systematic sampling and can be used to detect ecological phenomena. Moreover, we show that abundance indices were not affected by the effort of participants in the field or the delay between the observations and the collection of testimonials. Finally, we use the received testimonials to generate the longest ermine time series of relative abundance in the Canadian Arctic, spanning 29 years. Monitoring programs and research stations often have access to a pool of past participants (e.g. field workers, ecotourists) whose observations can be localized in time. As we strive to gain a deeper understanding of ecosystem functioning, tapping the memories of these people can provide valuable information on the past abundances of unmonitored populations and help answer hypotheses that would otherwise require years of systematic monitoring.
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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) | 0.003 | 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 it