The revolution of new genetic tools for the study of the ecology of rare or elusive species, using non-invasive approaches
Bibliographic record
Abstract
Since the last decade, the development of new molecular biology technologies and particularly the Next Generation Sequencing methods have revolutionized the study of biodiversity. Using non-invasive approaches (collect of faeces, hairs, urine, saliva..), these methods presently allow to detect the existence of cryptic species, to estimate gene flow among isolated populations, their population sizes but also to study the ecology of rare or elusive species, like their diets, their microbiomes, their sex ratio, their daily movements or their putative niche overlapping or hybridations with closely related species. More particularly, these new genetic methods are interesting for the study of threatened species, in order to propose the best management measures for them. Using different examples developed in my laboratory, I will enhance the interest of such studies, as complementary tools to other methodologies developed in the fields. As an example, these studies evidenced the most precise information concerning the diets of the Polar bear populations living in northern Canada, the European otter living in France, the Pyrenean Desman, the aquatic shrew or different African and Asian primate species, like the apes and the gorillas. These informations are of a prime interest to better understand the impact of habitat destruction on the food availability, and how these threatened species can adapt themselves to survive to global changes. On a more fundamental aspect, these studies evidenced how species having a close ecological niche, like the Pyrenean desman and the aquatic shrew can live in a same area, by shifting their diets, in order to avoid competition.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".