Spatial–temporal analysis of species range expansion: the case of the mountain pine beetle,<i>Dendroctonus ponderosae</i>
Bibliographic record
Abstract
Abstract Aim The spatial extent of western Canada’s current epidemic of mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae), is increasing. The roles of the various dispersal processes acting as drivers of range expansion are poorly understood for most species. The aim of this paper is to characterize the movement patterns of the mountain pine beetle in areas where range expansion is occurring, in order to describe the fine‐scale spatial dynamics of processes associated with mountain pine beetle range expansion. Location Three regions of Canada’s Rocky Mountains: Kicking Horse Pass, Yellowhead Pass and Pine Pass. Methods Data on locations of mountain pine beetle‐attacked trees of predominantly lodgepole pine ( Pinus contorta var. latifolia ) were obtained from annual fixed‐wing aircraft surveys of forest health and helicopter‐based GPS surveys of mountain pine beetle‐damaged areas in British Columbia and Alberta. The annual (1999–2005) spatial extents of outbreak ranges were delineated from these data. Spatial analysis was conducted using the spatial–temporal analysis of moving polygons (STAMP), a recently developed pattern‐based approach. Results We found that distant dispersal patterns (spot infestations) were most often associated with marginal increases in the areal size of mountain pine beetle range polygons. When the mountain pine beetle range size increased rapidly relative to the years examined, local dispersal patterns (adjacent infestation) were more common. In Pine Pass, long‐range dispersal (> 2 km) markedly extended the north‐east border of the mountain pine beetle range. In Yellowhead Pass and Kicking Horse Pass, the extension of the range occurred incrementally via ground‐based spread. Main conclusions Dispersal of mountain pine beetle varies with geography as well as with host and beetle population dynamics. Although colonization is mediated by habitat connectivity, during periods of low overall habitat expansion, dispersal to new distant locations is common, whereas during periods of rapid invasion, locally connected spread is the dominant mode of dispersal. The propensity for long‐range transport to establish new beetle populations, and thus to be considered a driver of range expansion, is likely to be determined by regional weather patterns, and influenced by local topography. We conclude that STAMP appears to be a useful approach for examining changes in biogeograpical ranges, with the potential to reveal both fine‐ and large‐scale patterns.
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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 | 0.002 |
| 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) | 0.001 | 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".