Summer Diet of Two White-tailed Deer, <em>Odocoileus virginianus</em>, Populations Living at Low and High Density in Southern Québec
Bibliographic record
Abstract
We investigated summer diets of two White-tailed Deer (Odocoileus virginianus) populations through rumen content analyses. Samples from 93 deer were collected in a low density, LD (1 deer/km2) and a high density, HD (14 deer/km2) area of southern Québec during the growing seasons of 1997 and 1998. Availability of preferred forage in forests was greater in LD than in HD, whereas agriculture covered a larger proportion of the area in HD than LD. Rumen composition differed between the two populations. Deer from HD consumed less forbs and leaves of shrubs and trees than did LD deer, whereas they consumed more fruits, grasses and farm crops. The rarity in HD rumens of food items common in LD, as well as in many parts of the White-tailed Deer range (i.e., Lilliacae), indicated that deer could not compensate for the rarity of preferred forest forage by increasing foraging time and had to feed on cultivated crops. Rumen contents of LD deer had a higher level of cell solubles and lignin, which reflected their greater reliance on quality forage growing in forests. Feeding habits and forage quality can explain why deer body size decreased in HD between the 1970s and 1990s whereas LD deer remained large.Nous avons examiné le régime alimentaire estival de deux populations de cerf de Virginie (Odocoileus virginianus) par l’analyse du contenu des rumens. Des échantillons ont été prélevés sur 93 cerfs provenant d’un secteur à faible densité de cerfs, LD, (1 cerf/km2) et d’un secteur à haute densité, HD, (14 cerfs/km2) du sud du Québec durant la saison de croissance des végétaux de 1997 et 1998. La disponibilité des aliments forestiers préférés des cerfs était plus grande dans LD que dans HD alors que la proportion du territoire agricole était supérieure dans HD. La composition des rumens des cerfs de chacune des régions différait. Les cerfs de HD ont consommé de moins grandes proportions de plantes herbacées et de feuilles d’arbustes et d’arbres que ceux du secteur LD, mais de plus grandes proportions de fruits, de graminées et de plantes agricoles. La rareté dans les rumens du secteur HD d’aliments communs dans ceux du secteur LD et dans plusieurs autres régions de l’aire de répartition du cerf (e.g., Lilliacae), indique que les cerfs du secteur HD ne pouvaient compenser pour la rareté de leurs aliments préférés par un accroissement de la durée de la quête alimentaire, et qu’ils devaient consommer des plantes agricoles. Les cerfs de LD ont mangé des aliments contenant plus de solubles cellulaires et de lignine que ceux de HD, ce qui reflète une consommation de plantes forestières de bonne qualité. Le régime alimentaire et la qualité de la nourriture peuvent expliquer pourquoi la taille des cerfs de HD a diminué entre les années 1970 et 1990 alors que celle des cerfs de LD est demeurée grande.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".