Remote Sensing Time Series for Modeling Invasive Species Distribution: A Case Study of Tamarix spp. in the US and Mexico
Bibliographic record
Abstract
Detecting invasive species and predicting their potential distribution are crucial \nto coordinate management responses. Remote sensing data are now available in several \nspatial and temporal resolutions and can supply environmental models with additional \ninformation. This study uses the Maximum Entropy algorithm to model the current \ndistribution of the saltcedar (Tamarix spp.) in the US and Mexico and to identify suitable \nhabitats, both already inhabited and not yet occupied. Tamarisk is restricted to specific \nhabitats such as riparian zones, wetlands and agricultural or disturbed areas, which are \ntypically not only characterized by climate. To describe vegetation phenology and thermal \nseasonality in these habitats, the study uses annual metrics of remotely sensed time series \nfrom 2001 to 2008 (Terra-MODIS Enhanced Vegetation Index and Land Surface \nTemperature) together with WorldClim bioclimatic data. By using occurrence records \nprimarily from the US we were able to model predictive maps of tamarisk distribution \ncorrelating very well to the known distribution in the US. For Mexico, where only very few \noccurrence records exist, we identified potential tamarisk habitats for substantial areas in \nBaja California, in the states of Sonora and Sinaloa and in the Central Mexican Plateau. \nThese predictive model results can be used to support the early detection and prevention of \nTamarix spp. invasion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".