Mapping spatiotemporal patterns of avocado expansion and land-use intensity in central Mexico and their effects on landscape connectivity
Bibliographic record
Abstract
Abstract Agricultural expansion and intensification are major drivers of global biodiversity loss, endangering natural habitats and ecosystem functions, such as pollination. In this study, we analyze the spatiotemporal dynamics of avocado frontier expansion and intensification from 2011 to 2019 and assess their effects on landscape connectivity, focusing on Michoacán, Mexico, the world’s leading avocado exporter. Using high-resolution satellite imagery combined with deep learning based on convolutional neural networks, we delineated avocado orchards and other land use/cover classes, mapped individual avocado tree crowns and irrigation ponds, and identified hotspots of expanding and intensifying avocado production. We used a circuit theory approach to evaluate the effects of avocado expansion and intensification on the connectivity of natural and semi-natural habitats. Our results reveal a rapid increase in avocado orchards, which expanded by 4175 ha—a growth from 27.9% to 37% in area—over the eight-year period. There was also a decline in rainfed agriculture by 3252 ha, and oak-pine forests by 1343 ha. We observed not only the expansion of the avocado frontier into forests but also an intensification of avocado production via increases in high-density plantations, irrigation ponds, and orchards prone to intensive pruning. Moreover, lower-intensity land-use classes, such as rainfed crops, were rapidly converted to avocado orchards. This expansion and intensification have led to increasing isolation of forest fragments. Although we identified routes that could facilitate the movement of species, the dense avocado monocultures continue to threaten the connectivity of natural and semi-natural habitats, causing notable losses of old-growth oak-pine forests and disrupting crucial ecological corridors. Our research underscores the adverse effects of avocado production on land use and landscape connectivity, emphasizing the need for sustainable management practices to ensure the long-term viability of avocado production systems and overall ecosystem functioning.
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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".