Simulating long‐distance seed dispersal in a dynamic vegetation model
Bibliographic record
Abstract
Abstract Aim Predicting the migration of vegetation in response to climate change is often done using a climate‐driven vegetation model; however, the assumption of full migration (where seeds are not limited by distance or barriers) is a common criticism. Previous efforts to incorporate limitations on seed dispersal have occurred exclusively in bioclimatic envelope models. This paper describes how limitations on seed dispersal were integrated into a physiologically based dynamic vegetation model, LPJ‐GUESS. Location An idealized landscape, representative of temperate and boreal forests in North America. Methods LPJ‐GUESS already simulates establishment, growth, reproduction and competition. I used a generic seed dispersal kernel to determine the probability of dispersal between grid cells, and a logistic function to determine the spread between patches within a grid cell. Plant functional types were parameterized to represent three temperate tree species, Acer, Pinus and Tsuga, by using published dispersal kernels and life‐history measurements. Simulations were run with full and limited migration, and compared with past vegetation migration rates. Results Using the old assumption of full migration, the entire landscape was colonized at the same time (migration rates of 270–380 m year−1). With the new limited dispersal, species colonized the landscape one row at a time, at rates which corresponded well with independent migration estimates based on genetic or pollen reconstructions (Acer, 141 m year−1; Pinus, 76 m year−1). Tsuga was the only species where simulated migration rates (85 m year−1) were quite a bit slower than historical migration estimates. Main conclusions The new model was able to simulate reasonable migration rates, which is a substantial improvement over previous assumptions of full migration. Migration estimates which include the effects of limitations on dispersal, demography, competition and plant physiology will also improve our understanding of how climate change and various other processes can influence plant range shifts.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".