Applied ecoimmunology: using immunological tools to improve conservation efforts in a changing world
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ecoimmunology is a rapidly developing field that explores how the environment shapes immune function, which in turn influences host-parasite relationships and disease outcomes. Host immune defence is a key fitness determinant because it underlies the capacity of animals to resist or tolerate potential infections. Importantly, immune function can be suppressed, depressed, reconfigured or stimulated by exposure to rapidly changing environmental drivers like temperature, pollutants and food availability. Thus, hosts may experience trade-offs resulting from altered investment in immune function under environmental stressors. As such, approaches in ecoimmunology can provide powerful tools to assist in the conservation of wildlife. Here, we provide case studies that explore the diverse ways that ecoimmunology can inform and advance conservation efforts, from understanding how Galapagos finches will fare with introduced parasites, to using methods from human oncology to design vaccines against a transmissible cancer in Tasmanian devils. In addition, we discuss the future of ecoimmunology and present 10 questions that can help guide this emerging field to better inform conservation decisions and biodiversity protection. From better linking changes in immune function to disease outcomes under different environmental conditions, to understanding how individual variation contributes to disease dynamics in wild populations, there is immense potential for ecoimmunology to inform the conservation of imperilled hosts in the face of new and re-emerging pathogens, in addition to improving the detection and management of emerging potential zoonoses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it