Keep in touch: Does spatial overlap correlate with contact rate frequency?
Bibliographic record
Abstract
Abstract Quantifying animal contact rate is crucial both in fundamental and applied studies to understand the evolution of sociality and predict the spread of infectious diseases. Researchers typically use home‐range overlap among individuals as a proxy of contact rate, assuming a positive correlation. However, very few studies have assessed how the correlation between home‐range overlap and contact rate may vary with ecological context. We used proximity loggers to quantify intraspecific contact rate among raccoons ( Procyon lotor ) and explored the correlation between contact rate and home‐range overlap in different seasons. We monitored 15 female raccoons that formed 121 dyads during summer 2010 and winter 2011. We compared contact rate with the 5 most common overlap indices: home‐range overlap proportion, home‐range overlap probability, utilization distribution overlap index (UDOI), volume of intersection index, and Bhattacharyya's affinity index. Our results generally supported the contention of a positive and significant correlation between home‐range overlap and intraspecific contact rate in raccoons. The strength of the relationship differed among seasons and indices, being weaker during winter than summer for home‐range overlap proportion and home‐range overlap probability. When contact rates were high, their frequency had stronger correlations with the UDOI and volume of intersection index indices than with the other indices. Our results suggest that the UDOI performs better than other indices, as we obtained a good contact rate–home‐range overlap correlation with this index with animals both aggregated and randomly distributed in space. © 2012 The Wildlife Society.
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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 | 0.001 | 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 | 0.001 |
| 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 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".