Wildlife Survival Estimation: Recent Advances and Future Directions
Bibliographic record
Abstract
Survival is a major determinant of animal population size and trend, and survival estimation has received considerable attention from population and conservation biologists seeking to understand patterns of abundance and numerical change in ecological systems. Although researchers commonly consider survival to be a population parameter, it is an attribute of individual animals. Because genetic, demographic, ecological, and environmental factors act upon individuals to influence survival, the objective of modern survival analysis is to quantify the role of such factors on mortality risk. In recent years, survival has been a prevalent topic of study in the Journal of Wildlife Management compared to other demographic attributes like productivity and dispersal (Fig. 1). However, survival tends to be more difficult to estimate properly than other demographic parameters (McCallum 2000, Fox 2001), especially among free-ranging animals where cryptic behavior patterns and difficulties in marking and monitoring often lead to small sample sizes and a high proportion of subjects succumbing to an unknown fate. These problems can limit the amount and quality of survival data obtained from ecological studies, and the challenge for researchers is to apply robust methodology that will make effective use of survival information obtained under sometimes constraining field conditions. Survival estimation has its early roots in human medicine and epidemiology (Oakes 2001), but in recent decades, considerable effort has focused on the development of reliable survival methods for application with free-ranging animals (Brownie et al. 1985, Burnham et al. 1987, Pollock et al. 1990, Lereton et al. 1992). In the last 20 years, substantial improvements have been made in this arena, both in terms of capture, marking, and monitoring techniques facilitating comprehensive survival assessment for free-ranging animals (Krebs 1999, Kenward 2001, Millspaugh and Marzluff 2001), and in the development of software programs allowing for the analysis of complex survival functions (Williams et al. 2002). Together, these changes have elicited a flurry of innovative survival research in animal ecology (Lebreton et al. 1992, Cam et al. 2002, DelGiudice et al. 2002, Dinsmore et al. 2002). These recent advances notwithstanding, there remains significant interest in the current state of survival estimation in wildlife research, particularly in terms of basic assumptions that may be difficult to meet in many research studies, or in the application of specialized analytical techniques requiring particular specification (Krebs 1999, Williams et al. 2002, Zens and Peart 2003, Rotella et al. 2004). Thus, a general review of current methodology and new developments in wildlife survival estimation should be timely and beneficial.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".