Hotspots of pest‐induced US urban tree death, 2020–2050
Bibliographic record
Abstract
Abstract Urban trees are important nature‐based solutions for future well‐being and liveability but are at high risk of mortality from insect pests. In the United States (US), 82% of the population live in urban settings and this number is growing, making urban tree mortality a matter of concern for most of its population. Until now, the magnitudes and spatial distributions of risks were unknown. Here, we combine new models of street tree populations in ~30,000 US communities, species‐specific spread predictions for 57 invasive insect species and estimates of tree death due to insect exposure for 48 host tree genera. We estimate that 1.4 million street trees will be killed by invasive insects from 2020 through 2050, costing an annualized average of US$ 30 M. However, these estimates hide substantial variation: 23% of urban centres will experience 95% of all insect‐induced mortality. Furthermore, 90% of all mortality will be due to emerald ash borer ( Agrilus planipennis , EAB), which is expected to kill virtually all ash trees ( Fraxinus spp.) in >6,000 communities. We define an EAB high‐impact zone spanning 902,500 km 2 , largely within the southern and central US, within which we predict the death of 98.8% of all ash trees. ‘Mortality hotspot cities’ include Milwaukee, WI; Chicago, IL; and New York, NY. We identify Asian wood borers of maple and oak trees as the highest risk future invaders, where a new establishment could cost US$ 4.9B over 30 years. Policy implications . To plan effective mitigation, forest pest managers must know which tree species in which communities will be at the greatest risk, as well as the highest risk species. We provide the first country‐wide, spatial forecast of urban tree mortality due to invasive insect pests. This framework identifies dominant pest insects and spatial impact hotspots, which can provide the basis for spatial prioritization of spread control efforts such as quarantines and biological control release sites. Our results highlight the need for emerald ash borer (EAB) early‐detection efforts as far from current infestations as Seattle, WA. Furthermore, these findings produce a list of biotic and spatiotemporal risk factors for future high‐impact US urban forest insect pests.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".