Climate driven range divergence among host species affects range-wide patterns of parasitism
Bibliographic record
Abstract
Species interactions like parasitism influence the outcome of climate-driven shifts in species ranges. For some host species, parasitism can only occur in that part of its range that overlaps with a second host species. Thus, predicting future parasitism may depend on how the ranges of the two hosts change in relation to each other. In this study, we tested whether the climate driven species range shift of Odocoileus virginianus (white-tailed deer) accounts for predicted changes in parasitism of two other species from the family Cervidae, Alces alces (moose) and Rangifer tarandus (caribou), in North America. We used MaxEnt models to predict the recent (2000) and future (2050) ranges (probabilities of occurrence) of the cervids and a parasite Parelaphostrongylus tenuis (brainworm) taking into account range shifts of the parasite’s intermediate gastropod hosts. Our models predicted that range overlap between A. alces / R. tarandus and P. tenuis will decrease between 2000 and 2050, an outcome that reflects decreased overlap between A. alces / R. tarandus and O. virginianus and not the parasites, themselves. Geographically, our models predicted increasing potential occurrence of P. tenuis where A. alces / R. tarandus are likely to decline, but minimal spatial overlap where A. alces / R. tarandus are likely to increase. Thus, parasitism may exacerbate climate-mediated southern contraction of A. alces and R. tarandus ranges but will have limited influence on northward range expansion. Our results suggest that the spatial dynamics of one host species may be the driving force behind future rates of parasitism for another host species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".