Can spatial isolation help predict dispersal-limited sites for native species restoration?
Bibliographic record
Abstract
When the distribution of species is limited by propagule supply, new populations may be initiated by seed addition, but identifying suitable sites for efficiently targeted seed addition remains a major challenge for restoration. In addition to the biotic or abiotic variables typically used in species distribution models, spatial isolation from conspecifics could help predict the suitability of unoccupied sites. Site suitability might be expected to increase with spatial isolation after other factors are accounted for, since isolation increases the chance that a site is unoccupied only because of propagule limitation. For two native annual forbs in Californian grasslands, we combined experimental seeding and niche modeling to ask whether suitability of unoccupied sites could be predicted by spatial variables (either distances from, or densities of, conspecific populations), either by themselves or in combination with niche models. We also asked whether experimental tests of these predictions held up not only in the short term (one year), but also in the longer term (three years). For Lasthenia californica, seed additions were only successful relatively near existing populations. For Lupinus nanus, seeding success was low and was positively related to the number of conspecifics within 1 km. For both species, a few previously unoccupied sites remained occupied three years after seeding, but this subset was not predictable based on either spatial or niche variables. Seed addition alone may be a limited means of native forb restoration if suitable unoccupied sites are either rare or unpredictable, or if they tend to be close to where the species already occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.049 | 0.001 |
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 teacher head, 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".