Effects of Rotational Shepherding on Plant Dispersal and Gene Flow in Fragmented Calcareous Grasslands
Bibliographic record
Abstract
Understanding dispersal and gene flow in human-modified landscapes is crucial for effective conservation. Seed dispersal governs colonization, recruitment, and distribution of plant species, whereas both pollen and seed dispersal determine gene flow among populations. This PhD thesis tests the effect of rotational shepherding on seed dispersal and gene flow in fragmented calcareous grasslands. Calcareous grasslands (Gentiano-Koelerietum pyramidatae vegetation) in Central Europe are semi-natural communities traditionally used for rotational grazing that experienced a decline of plant species during the 20th century due to abandonment of shepherding. This PhD profits from a management project started in 1989 in Bavaria, Germany to reconnect previously abandoned calcareous grasslands in three non-overlapping shepherding systems. Two vegetation surveys in 1989 and 2009 revealed colonizations in previously abandoned grasslands reconnected by shepherding. First, I propose a comprehensive approach to identify determinants of community-level patch colonization rates based on 48 habitat specialist plants by testing competing models of pre-dispersal and dispersal effects and accounting for post-dispersal effects. Mean source patch species occupancy in 1989, and structural elements in focal patches related to establishment explained community-level patch colonization rates. Secondly, by adapting the community analysis to all 31 individual species of the same community with sufficient data, I corroborate the role of shepherding to support dispersal for a range of species, even if they lack seed morphological traits related to zoochory. Thirdly, for the habitat specialist Dianthus carthusianorum, I genotyped 1,613 individuals from 64 populations at eleven microsatellites to test the effect of dispersal by sheep on spatial genetic structure at the landscape scale. Genetic distances between grazed patches of the same herding system were related to distance along herding routes, whereas ungrazed patches showed isolation by geographic distance. Lastly, within individual grassland patches, shepherding significantly decreases the degree of relatedness among neighboring individuals (kinship structure) and increases genetic diversity. My thesis contributes towards understanding the effects of zoochory on spatial dynamics in plant populations across scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".