Estimation of Body‐Size Traits by Photogrammetry in Large Mammals to Inform Conservation
Bibliographic record
Abstract
Photography, including remote imagery and camera traps, has contributed substantially to conservation. However, the potential to use photography to understand demography and inform policy is limited. To have practical value, remote assessments must be reasonably accurate and widely deployable. Prior efforts to develop noninvasive methods of estimating trait size have been motivated by a desire to answer evolutionary questions, measure physiological growth, or, in the case of illegal trade, assess economics of horn sizes; but rarely have such methods been directed at conservation. Here I demonstrate a simple, noninvasive photographic technique and address how knowledge of values of individual-specific metrics bears on conservation policy. I used 10 years of data on juvenile moose (Alces alces) to examine whether body size and probability of survival are positively correlated in cold climates. I investigated whether the presence of mothers improved juvenile survival. The posited latter relation is relevant to policy because harvest of adult females has been permitted in some Canadian and American jurisdictions under the assumption that probability of survival of young is independent of maternal presence. The accuracy of estimates of head sizes made from photographs exceeded 98%. The estimates revealed that overwinter juvenile survival had no relation to the juvenile's estimated mass (p < 0.64) and was more strongly associated with maternal presence (p < 0.02) than winter snow depth (p < 0.18). These findings highlight the effects on survival of a social dynamic (the mother-young association) rather than body size and suggest a change in harvest policy will increase survival. Furthermore, photographic imaging of growth of individual juvenile muskoxen (Ovibos moschatus) over 3 Arctic winters revealed annual variability in size, which supports the idea that noninvasive monitoring may allow one to detect how some environmental conditions ultimately affect body growth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".