RETHINKING RARE: NOVEL APPROACHES TO RARE SPECIES MONITORING AND CONSERVATION
Bibliographic record
Abstract
Conservation of rare species is widely valued and important for ecosystems. Unfortunately, many of the approaches to conserve rare species have been developed with common species (e.g., harvested species) which have larger populations and targeted objectives. Conservation of rare species is difficult in part because of problems created by scarcity and low information. With low information, learning leads to new questions and the utility of information in decisions can quickly become obsolete. Therefore, monitoring strategies that can adapt as well as provide information tailored to relevant decisions are needed. To address rare species monitoring, I developed a long-term monitoring approach for rare species called goal efficient monitoring (GEM). GEM allows monitoring questions to evolve as we obtain information. GEM includes sampling rules connected to a Bayesian integrated population model (IPM), which allows for changing questions and data collection while maintaining long-term inference. For example, GEM sampling rules work when populations are small (less than 10 individuals) and provide guidance to adjust monitoring observations if the population gets large (over 100 individuals), all while maintaining the same long-term inference because of the IPM structure. I outline the GEM approach using Canada lynx (Lynx canadensis), which is Threatened under the Endangered Species Act. To test GEM, I simulated 100 small populations with constant demographic rates for 11 years, applied GEM sampling rules to simulate observations, and predicted population values with the GEM model. On average, the predicted range of values from the GEM model contained the true values 97.1% of the time. These and other results contained within demonstrate how a GEM approach can provide long-term inference for rare species while addressing changing information needs. To address the problem of rare species information that is tailored to decisions made with rare species information, I propose the use of processes from the professional field of Design to reframe the user needs of the rare species information. I provide an overview of how some Design methods are already in use in conservation and how adopting Design processes more formally through the creation of the field of conservation design may aid in rare species conservation.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".