An introduction to event history analyses for ecologists
Bibliographic record
Abstract
Abstract Efforts to understand the emergence of an event require our ability to measure and understand the dynamics between time in a state (e.g., being alive or a behavior) and the outcome of the state. Studying the main drivers that affect changes in state over time allows researchers to better understand population dynamics and evolutionary processes. Event history analyses provide a range of theoretical and empirical tools to explore the emergence of an event. Their use is still restricted in ecology; however, they are commonly used in human demography. Event history analysis is a powerful tool for measuring the probability that an event occurs at timet. Here, we provide an introductory guide for ecologists who are interested in exploring event history analyses in their research. In the first part of this article, we outline key concepts in event history analyses and present a decision tree, statistical techniques, and their applications to ecological questions. To introduce practical applications of event history analyses, we provide four detailed tutorials, stemming from observational and longitudinal records of events in mammalian and avian species, along with relevant R scripts. We then explain how to interpret and present results of such analyses. Our results show that event history analyses are useful to quantify the effect of factors on the emergence of events. We conclude by highlighting additional strengths, pitfalls, and limitations researchers should be aware of when using such methods. We foresee the use of event history analyses for ecological studies.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".