Humans Strengthen Bottom-Up Effects and Weaken Trophic Cascades in a Terrestrial Food Web
Bibliographic record
Abstract
Ongoing debate about whether food webs are primarily regulated by predators or by primary plant productivity, cast as top-down and bottom-up effects, respectively, may becoming superfluous. Given that most of the world's ecosystems are human dominated we broadened this dichotomy by considering human effects in a terrestrial food-web. We studied a multiple human-use landscape in southwest Alberta, Canada, as opposed to protected areas where previous terrestrial food-web studies have been conducted. We used structural equation models (SEMs) to assess the strength and direction of relationships between the density and distribution of: (1) humans, measured using a density index; (2) wolves (Canis lupus), elk (Cervus elapahus) and domestic cattle (Bos taurus), measured using resource selection functions, and; (3) forage quality, quantity and utilization (measured at vegetation sampling plots). Relationships were evaluated by taking advantage of temporal and spatial variation in human density, including day versus night, and two landscapes with the highest and lowest human density in the study area. Here we show that forage-mediated effects of humans had primacy over predator-mediated effects in the food web. In our parsimonious SEM, occurrence of humans was most correlated with occurrence of forage (β = 0.637, p<0.0001). Elk and cattle distribution were correlated with forage (elk day: β = 0.400, p<0.0001; elk night: β = 0.369, p<0.0001; cattle day: β = 0.403, p<0.0001; cattle, night: β = 0.436, p<0.0001), and the distribution of elk or cattle and wolves were positively correlated during daytime (elk: β = 0.293, p <0.0001, cattle: β = 0.303, p<0.0001) and nighttime (elk: β = 0.460, p<0.0001, cattle: β = 0.482, p<0.0001). Our results contrast with research conducted in protected areas that suggested human effects in the food web are primarily predator-mediated. Instead, human influence on vegetation may strengthen bottom-up predominance and weaken top-down trophic cascades in ecosystems. We suggest that human influences on ecosystems may usurp top-down and bottom-up effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".