IMPACT OF MINIMUM WINTER TEMPERATURES ON THE POPULATION DYNAMICS OF<i>DENDROCTONUS FRONTALIS</i>
Bibliographic record
Abstract
Predicting population dynamics is a fundamental problem in applied ecology. Temperature is a potential driver of short-term population dynamics, and temperature data are widely available, but we generally lack validated models to predict dynamics based upon temperatures. A generalized approach involves estimating the temperatures experienced by a population, characterizing the demographic consequences of physiological responses to temperature, and testing for predicted effects on abundance. We employed this approach to test whether minimum winter temperatures are a meaningful driver of pestilence from Dendroctonus frontalis (the southern pine beetle) across the southeastern United States. A distance-weighted interpolation model provided good, spatially explicit, predictions of minimum winter air temperatures (a putative driver of beetle survival). A Newtonian heat transfer model with empirical cooling constants indicated that beetles within host trees are buffered from the lowest air temperatures by approximately 1-4 degrees C (depending on tree diameter and duration of cold bout). The life stage structure of beetles in the most northerly outbreak in recent times (New Jersey) were dominated by prepupae, which were more cold tolerant (by >3 degrees C) than other life stages. Analyses of beetle abundance data from 1987 to 2005 showed that minimum winter air temperature only explained 1.5% of the variance in interannual growth rates of beetle populations, indicating that it is but a weak driver of population dynamics in the southeastern United States as a whole. However, average population growth rate matched theoretical predictions of a process-based model of winter mortality from low temperatures; apparently our knowledge of population effects from winter temperatures is satisfactory, and may help to predict dynamics of northern populations, even while adding little to population predictions in southern forests. Recent episodes of D. frontalis outbreaks in northern forests may have been allowed by a warming trend from 1960 to 2004 of 3.3 degrees C in minimum winter air temperatures in the southeastern United States. Studies that combine climatic analyses, physiological experiments, and spatially replicated time series of population abundance can improve population predictions, contribute to a synthesis of population and physiological ecology, and aid in assessing the ecological consequences of climatic trends.
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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 | 0.000 | 0.000 |
| 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".