Modeling Urban Host Tree Distributions for Invasive Forest Pests Using a Multi-Step Approach
Bibliographic record
Abstract
Many invasive pest species currently impacting forested ecosystems in North America first appeared in urban forests. Despite serving as critical gateways for the spread of forest pests, urban forests remain less well documented than their “natural” forest counterparts. Only a small percentage of communities in the US and Canada have completed any sort of urban forest inventory, and these inventories usually have been limited to street trees. We devised a multi-step approach that utilizes the available local inventory data to model urban host tree distributions at a regional scale. We illustrate the approach for three tree genera – ash (Fraxinus), maple (Acer), and oak (Quercus) – that are associated with high-profile insect pests. Available inventory data included 60 sample-based inventories of entire cities (i-Tree Eco inventories) and 500 street tree inventories. First, we used co-kriging to estimate the whole-city tree compositions based on street tree inventories. Next, we used boosted decision trees to model the proportion of the total basal area (as a proxy for forest volume) occupied by each genus in non-inventoried communities as a function of a suite of environmental and demographic variables. We then modeled total urban forest basal area on canopy cover of these communities using a generalized additive model. We combined these estimates to construct region-wide urban distribution maps for each genus. By merging these maps with similar data on natural forests, we are able to provide a more complete host setting for spread modeling efforts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".