Multi-species mechanistic model of functional response provides new insights into predator-mediated interactions in a vertebrate community
Bibliographic record
Abstract
1. A bstract Prey handling processes are considered a key driver of short-term positive indirect effects between prey sharing the same predator. However, a growing body of research indicates that predators are rarely limited by such processes in the wild. Density-dependent changes in predator foraging be-havior can also generate positive indirect effects but they are rarely included as explicit functions of prey densities in functional response models. With the aim of untangling proximate drivers of species interactions in natural communities and improve our ability to quantify interaction strength, we extended the Holling multi-species model by including density-dependent changes in predator foraging behavior. Our model, based on species traits and behavior, was inspired by the verte-brate community of the arctic tundra, where the main predator (the arctic fox) is an active forager feeding primarily on cyclic small rodent (lemming) populations and eggs of various tundra-nesting bird species. Short-term positive indirect effects of lemmings on birds have been documented over the circumpolar Arctic but the underlying proximate mechanisms remain poorly known. We used a unique data set, containing high-frequency GPS tracking, accelerometer, behavioral, and experimental data to parameterize the multi-species model, and a 15-year time series of prey densities and bird nesting success to evaluate inter-action strength between species. Our results showed that: (i) prey handling processes play a minor role in our system and (ii) density-dependent changes in predator foraging behavior can be the proximate drivers of a predominant predator-mediated interaction observed in the arctic tundra. Mechanisms outlined in our study have been little studied and may play a significant role in natural systems. We hope that our study will provide a useful starting point to build mechanistic models of predation, and we think that our approach could conceivably be applied to a broad range of food webs.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".