Demographic compensation does not rescue populations at a trailing range edge
Bibliographic record
Abstract
ABSTRACT As climate change shifts species' climatic envelopes across the landscape, equilibrium between geographic ranges and niches is likely diminishing due to time lags in demography and dispersal. If a species' range and niche are out of equilibrium, then population performance should decrease from cool, “leading” range edges, where populations are expanding into recently ameliorated habitats, to warm, “trailing” range edges, where populations are contracting from newly unsuitable areas. Population contraction signals that compensatory changes in vital rates are insufficient to buffer population growth from deteriorating environments. Life history theory predicts tradeoffs between fast development, high reproduction, and short longevity at low latitudes and slow development, less frequent but multiple bouts of reproduction, and long lifespan at high latitudes. If demographic compensation is driven by life history evolution, compensatory negative correlations in vital rates may be associated with this fast-slow continuum. An outstanding question is whether range limits and range contractions reflect inadequate compensatory life history shifts along environmental gradients, causing population growth rates to fall below replacement levels at range edges. We surveyed demography of 32 populations of the scarlet monkeyflower ( Erythranthe cardinalis ) spanning 11° latitude in western North America and used integral projection models to infer population dynamics and assess demographic compensation. Population growth rates decreased from north to south, consistent with leading-trailing dynamics. Southern populations are declining due to reduced survival, growth, and recruitment, despite compensatory increases in reproduction and faster life history characteristics, suggesting that demographic compensation will not rescue populations at the trailing range edge. SIGNIFICANCE STATEMENT While climate change is causing poleward shifts in many species' geographic distributions, some species' ranges have remained stable, particularly at low-latitude limits. One explanation for why some species' ranges have not shifted is demographic compensation, whereby declines in some demographic processes are offset by increases in others, potentially buffering populations from extinction. However, we have limited understanding of whether demographic compensation can prevent collapse of populations facing climate change. We examined the demography of natural populations of a perennial herb spanning a broad latitudinal gradient. Despite increases in reproduction, low-latitude populations declined due to diminished survival, growth, and recruitment. Thus, demographic compensation may not be sufficient to rescue low-latitude, warm-edge populations from extinction.
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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".