Applied ecoimmunology: using immunological tools to improve conservation efforts in a changing world
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.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".