Links between energy budgets, somatic condition, and life history reveal heterogeneous energy management tactics in a group-living mesocarnivore
Bibliographic record
Abstract
BACKGROUND: Optimal management of voluntary energy expenditure is crucial to the survival and reproductive success of wild animals. Nevertheless, a growing appreciation of inter-individual variation in the internal state driving movement suggests that individuals may follow different, yet equally optimal tactics under the same environmental conditions. However, few studies in wild populations have investigated the occurrence and demographic context of different contemporaneous energetic expenditure tactics. Here, we explore this neglected aspect of energy budgeting in order to determine the effect of life-history traits such as age and reproductive status on the co-occurrence of different energy-budgeting tactics in wild populations. METHODS: We investigated inter-individual heterogeneity in energy expenditure within a wild population of European badgers (Meles meles) by quantifying individual overall dynamic body acceleration (ODBA, from tri-axial accelerometry collars) and total daily energy expenditure (DEE, from doubly-labelled water) during 6-9 day deployments and dosing periods over six different seasons (spring, summer, and autumn) in 2018-2019. We obtained ODBA values for 41 deployments (24 unique badgers) and DEE measurements for 41 dosings (22 unique badgers). We then evaluated correlations between these energetic metrics and computed individual ratios of ODBA/DEE as a proxy for the proportion of total energy spent on activity. We measured the impact of alternative ODBA/DEE ratios on body condition, and use survival models constructed using 29 years of demographic data from the same population to situate body-condition changes in the context of age and reproductive status. RESULTS: Both ODBA and DEE were highly variable between individuals and exhibited season-specific relationships with individual body condition and life-history factors. DEE scaled allometrically with body weight, but only in summer and autumn; post-reproductive female badgers were lighter than other badgers during the spring but expended on average 350 kJ/day more than predicted from allometric scaling. Older badgers expended significantly less energy on movement during the summer than did younger adults. The ratio of ODBA to DEE (OD) provides a measure of proportional investment into movement. This ratio correlated more significantly with next-season body condition than either energetic metric did independently. However, the majority of individuals with high OD ratios were either younger badgers or reproductive females, for which lower body condition typically presented less of a mortality risk in previous analyses of this population. CONCLUSIONS: Within a single population under the same environmental conditions, we found wide inter-individual variation in both mechanical and total energy expenditure. The adoption of different tactics aligns with relationships between life-history parameters and mortality risk previously studied within the population. Crucially, younger badgers and reproductive females appeared able to tolerate energy expenditure tactics that depleted their body condition more than other badgers. These findings provide a mechanism by which differences in individual energetic context set by life history can maintain heterogeneity in wild populations, providing a wide range of potential energetic tactics under changing environmental conditions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".