The socioeconomic drivers of invasive plant cover in rural Galapagos
Bibliographic record
Abstract
Abstract The unique biodiversity of the Galapagos Islands faces a significant threat from invasive plants, particularly in the highlands. Invasive plant occurrence is expected to shift in response to changing rural socioeconomic factors, further exacerbating the threat to native ecosystems. So far, limited research has integrated socioeconomic factors as drivers of invasive plant cover changes across the rural landscape. Our study employed stakeholder surveys, plant cover models, and spatial and statistical analyses to identify socioeconomic factors impacting the cover of commonly used or controlled invasive plant species (guava, Cuban cedar, blackberry) in the rural areas of Santa Cruz and San Cristobal Islands. Relevant socioeconomic factors were selected through a ranked predictor analysis in species cover models using random forest algorithms. Both biophysical and socioeconomic predictors were included to increase model comprehensiveness. We found that biophysical factors—such as climate, soil properties, and topography—were strong predictors of Cuban cedar cover. In contrast, socioeconomic factors, soil properties, and climate were better predictors of guava. Statistical analyses revealed that landowners’ primary income source and land use had a significant influence on guava cover. Farms dependent on agriculture had lower guava cover, while those focusing on tourism had the highest. The quality of available data limited results for blackberry. Our study highlights relevant factors that should be considered for invasive plant management in rural Galapagos. It quantifies the effect of key socioeconomic variables on guava cover on Santa Cruz and San Cristobal Islands.
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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".