Fecal hormones as a non‐invasive population monitoring method for reindeer
Bibliographic record
Abstract
Abstract Proper management of threatened species requires knowledge of population sizes and structures, however current techniques to gather this information are generally impractical and costly and can be stressful on the animals. Non‐invasive methods that can produce high quality and accurate results are better alternatives. In winter 2010, we collected blood and fecal samples from 2 reindeer (Rangifer tarandus) populations (Kaamanen, Finland and Svalbard, Norway) to investigate the feasibility of using fecal progesterone metabolites to help estimate the reproductive status, the sex, and the age structures of the populations. We first examined the relationship between plasma progesterone and fecal progesterone metabolite concentrations. We further assessed whether fecal progesterone metabolite levels would clearly differ among calf, yearling, and adult and between pregnant and non‐pregnant females. We quantified fecal progesterone metabolites (using enzyme immunoassay) and plasma progesterone (using radio immunoassay) of females and males of different ages from the 2 herds. We found in both populations that fecal progesterone metabolite levels reflected plasma progesterone concentrations. However, the range of fecal progesterone metabolite concentration was much wider in Finland than in Svalbard, possibly due to differences in diet or body condition. We determined a threshold value of 1.31 ng/ml plasma progesterone and 2025.93 ng/g dried fecal progesterone metabolites to identify pregnant reindeer from non‐pregnant animals with 100% accuracy. We found a significant difference in fecal progesterone metabolite concentrations only between calves and yearlings/adults in Finland. We could not differentiate among males, non‐pregnant adults, or calves of either sex; therefore identification of sex may have to rely on the use of DNA techniques. Our results suggest that hormone concentration, in combination with fecal DNA and pellet morphometry techniques, may provide important population parameters and is a valuable tool for the monitoring of reindeer and may have an application for threatened populations of woodland caribou throughout the winter and early spring. © 2011 The Wildlife Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".