Assisting seed dispersers to restore oldfields: An individual‐based model of the interactions among badgers, foxes and Iberian pear trees
Bibliographic record
Abstract
Abstract Increasing land abandonment in many areas of the world presents an opportunity for ecosystem recovery, which is often driven by seed dispersal by vertebrate frugivores. However, we are far from understanding the most effective way of using common management actions (i.e. planting fruiting trees) to stimulate animal seed dispersal and thus the restoration of human‐altered abandoned habitats. To investigate how to stimulate animal seed dispersal, we combined long‐term field data with individual‐based, spatially explicit simulation models. We used our approach to assess the effectiveness of contrasting Iberian pear Pyrus bourgaeana planting strategies in enhancing restoration of abandoned lands through seed dispersal by red foxes Vulpes vulpes and Eurasian badgers Meles meles in the Doñana World Biosphere Reserve (South West Spain). Our simulation results indicate that planting trees in an aggregated fashion is less efficient in terms of seed arrival than planting them regularly or randomly. For aggregated planted trees, the increase in the area of the oldfield that received seeds was only 7%–9% compared to the baseline scenario of no intervention, whereas for regularly distributed planted trees the increment was up to 40%. Doubling the number of planted P. bourgaeana trees appeared cost‐effective for regular and random tree distributions, but not for the aggregated one. For example, while doubling the number of trees planted regularly leads up to 12% increase in the number of seeds arriving into the oldfield, no increment on the number of arrived seeds was detected when trees were planted aggregately. Synthesis and applications . Choosing the spatial distribution and density of planted trees in abandoned lands depends on a number of ecological and socio‐economical factors. Given our results, the strong seed dispersal limitation of the target tree population and that our study site was fully protected for conservation, planting Pyrus bourgaeana trees regularly appeared to be the most efficient strategy to enhance seed arrival into the target oldfield. Combining long‐term field data with individual‐based, spatially explicit simulation models have the potential to guide local restoration efforts in diverse human‐altered habitats and thus bridge the existing gap between basic and applied research on animal seed dispersal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".