Simulating long-term effectiveness and efficiency of management scenarios for an invasive grass
Bibliographic record
Abstract
Resource managers are often faced with trade-offs in allocating limited resources to manage plant invasions. These decisions must often be made with uncertainty about the location of infestations, their rate of spread and effectiveness of management actions. Landscape level simulation tools such as state-and-transition simulation models (STSMs) can be used to evaluate the potential long term consequences of alternative management strategies and help identify those strategies that make efficient use of resources. We analyzed alternative management scenarios for African buffelgrass (<em>Pennisetum ciliare</em> syn. <em>Cenchrus ciliaris</em>) at Ironwood Forest National Monument, Arizona using a spatially explicit STSM implemented in the Tool for Exploratory Landscape Scenario Analyses (TELSA). Buffelgrass is an invasive grass that is spreading rapidly in the Sonoran Desert, affecting multiple habitats and jurisdictions. This invasion is creating a novel fire risk and transforming natural ecosystems. The model used in this application incorporates buffelgrass dispersal and establishment and management actions and effectiveness including inventory, treatment and post-treatment maintenance. We simulated 11 alternative scenarios developed in consultation with buffelgrass managers and other stakeholders. The scenarios vary according to the total budget allocated for management and the allocation of that budget between different kinds of management actions. Scenario results suggest that to achieve an actual reduction and stabilization of buffelgrass populations, management unconstrained by fiscal restrictions and across all jurisdictions and private lands is required; without broad and aggressive management, buffelgrass populations are expected to increase over time. However, results also suggest that large upfront investments can achieve control results that require relatively minimal spending in the future.Investing the necessary funds upfront to control the invasion results in the most efficient use of resources to achieve lowest invaded acreage in the long-term.
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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 | 0.001 | 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 | 0.002 |
| 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".