Linking differences in personality to demography in the wandering albatross
Bibliographic record
Abstract
Abstract Population dynamics are shaped by individual differences. With a good understanding of the relationships between individual differences and vital rates, population models can be improved to yield more realistic and detailed demographic projections. Personality, i.e., consistent individual differences in behaviour, is expected to shape individual differences in performance. Yet, an empirical quantification of its impact on population dynamics is currently lacking. Here, we developed and analysed a three-dimensional hyperstate population model that accounts for three sources of individual differences simultaneously in its structure: age, breeding state and boldness as a measure of personality. We parameterized our model using empirical demographic and boldness data on the wandering albatross ( Diomedea exulans ) population from the Crozet archipelago. We conducted sensitivity analyses to quantify the relative importance of boldness. We then simulated scenarios of increased strength of relationship between boldness and three vital rates (survival, breeding probability and breeding success) to explore conditions under which shifts in boldness distribution could be observed in the future. We showed that the sensitivity of population growth rate to changes in vital rates followed the normal distribution of boldness within the population. Indeed, population growth rate was much less sensitive to changes in the vital rates of extreme shy or bold individuals, compared to that of individuals of intermediate boldness. Overall, sensitivity of population growth rate was greater for survival than reproductive rates across all three dimensions. Our simulations showed that increasing the strength of the relationship between boldness and survival would yield the greatest shift in boldness distribution over time compared to breeding probability and breeding success. However, shifts in boldness distribution appeared constrained by the low heritability ( < 0.2) value and the large variance in boldness in this population. Our study provides an important contribution to our understanding of the role of personality in shaping the population dynamics of wild species. In the face of global change, our approach offers a promising avenue to predict the potential for behavioral adaptation. More generally, our approach may help to unravel the complex interplay between individual variations in any (or many) traits and population dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".