Human‐induced reduction in mammalian movements impacts seed dispersal in the tropics
Bibliographic record
Abstract
Seed dispersal is a key process affecting the structure, composition and spatial dynamics of plant populations. Numerous plant species in the tropics rely upon animals to disperse their seeds. Humans have altered mammalian movements, which will likely affect seed dispersal distances (SDD). Altered SDD may have a range of consequences for plant communities including reduced seedling recruitment and plant biomass, seed trait homogenization, altered gene flow and a reduced capacity to respond to environmental changes. Therefore, modelling the consequences of altered animal behaviour on ecosystem processes is important for predicting how ecosystems will respond to human impacts. While previous research has focused on the link between animal species extirpation and SDD, it remains unclear how changes in mammalian movement will impact SDD. Here we implemented a mechanistic modelling approach to examine how mammalian movement reductions impact SDD in the tropics. We combined allometric theory with a mechanistic seed dispersal model to estimate SDD via the movement of 37 large frugivorous mammals (> 10 kg) in the tropics under different levels of human footprint, a global proxy of direct and indirect human disturbances. Our results suggest that assemblage‐level SDD reductions are estimated to be up to 80% across the tropics in response to human disturbance. This is particularly the case in areas with high human impact such as agricultural landscapes and suburban areas. The region with the largest reductions in SDD was the Asia‐Pacific with average reductions of 25%, followed by Central–South America (16%) and then Africa (15%). Our study provides insights into how human‐induced changes in movement behaviour of large mammals could translate into altered ecosystem functioning.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".