Capturing seasonal variations in faecal nutrient content from tundra herbivores using near infrared reflectance spectroscopy
Bibliographic record
Abstract
Herbivores contribute to nutrient cycling in tundra ecosystems through their waste (e.g., faeces, urine). However, their contribution might vary among species and over time during the growing season likely due to differences in body size, digestive physiology, and variations in diet composition and quality. Capturing fine-scale variability requires intensive sampling, but traditional wet-lab methods for measuring nutrient concentration and stoichiometry in animal faeces are prohibitively expensive. To address this challenge, we developed a low-cost alternative using Near-Infrared Reflectance Spectroscopy (NIRS). We calibrated a general model for the main Icelandic tundra herbivores (i.e., pink-footed goose, Anser brachyrynchus, reindeer, Rangifer tarandus and sheep, Ovis aries) to assess faecal nutrient concentrations (nitrogen, phosphorus, and carbon) and stoichiometry (C:N, C:P, N:P). This was achieved using a set of 191 fresh faecal samples scanned with NIRS and analysed by traditional wet-lab methods. The multispecies models explained between 76 and 91 % of variation between samples. We then applied the models to over 300 samples and assessed changes in faecal nutrient concentration, and stoichiometry of the three herbivores throughout the growing season.We found that faecal quality varied between herbivore species, with sheep and reindeer generally having more similar nutrient concentrations and stoichiometry than geese. Seasonality also affected faecal nutrient content, with a general decrease in N and P concentrations over the growing season and an increase in C:N and C:P ratios, especially in geese. Geese contributed disproportionately to the nutrient pools of Icelandic rangelands due to their high defecation rate and large population. These results provide important insights into how different herbivore species can influence the biogeochemistry of nutrient-limited tundra rangelands throughout the growing season, and a general model for faecal nutrient content of tundra herbivores.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".