Seed masting causes fluctuations in optimum litter size and lag load in a seed predator
Bibliographic record
Abstract
The episodic production of large seed crops by some perennial plants, is referred to as masting and is known to increase seed escape by alternately starving and swamping seed predators. These pulses of resources, however, might also act as an agent of selection on the life histories of seed predators, which could indirectly enhance seed escape by inducing an evolutionary load on seed predator populations. Lag loads in seed predators could result from mast-induced shifts in optimum phenotypes that exceed the capacity of seed predators to adaptively track optimum phenotypes through phenotypic plasticity. Alternatively, masting could generate mismatches in selection across generations, where adaptation to the parental environment leads to maladaptation in the offspring environment. Here we measured natural selection on female North American red squirrels (Tamiasciurus hudsonicus) across 28 years and five white spruce (Picea glauca) masting events. Red squirrel litter sizes were similar to optimum litter sizes during non-mast years, but were well below optimum litter sizes during resource-rich mast years. Mast events, therefore caused selection for larger litters (B = 0.25) and a lag load (L = 0.25) on red squirrels during mast years. Furthermore, we found that the annual fitness of spruce trees was negatively related to the local density of squirrels during mast years, indicating that the observed lag load on squirrels enhanced the number of spruce cones escaping squirrel predation. Although, the frequency of mast events and the demography of red squirrels were such that offspring and parents often experienced opposite environments with respect to the mast, we found no effect of environmental mismatches across generations on either offspring survival or population growth. Instead, squirrels plastically increased litter sizes in anticipation of mast events, which partially, although not completely, reduced the lag load resulting from this change in food availability. Variable selection on litter size caused by white spruce mast events, therefore, induced a lag load on the population of red squirrels that was not affected by whether individual squirrels were born during mast (matching) or non-mast (mismatching) conditions.
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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 | 0.000 |
| 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".