Fluvial seed dispersal of riparian trees: transport and depositional processes
Bibliographic record
Abstract
Abstract Fluvial seed dispersal considers both the transport and deposition of seeds where channel geomorphic structures, hydrology and seed dispersal traits contribute to transport times and depositional locations. This study examines the influence of stream flow patterns on fluvial seed dispersal of buoyant white alder ( Alnus rhombifolia ) seeds by applying a one‐dimensional transport model. Conceptually, the model separates the stream into two components: (i) the main channel where the seeds are transported downstream; and (ii) the transient storage zone where seeds are temporarily detained or deposited on the river bank. Transport processes are characterized by an advection–dispersion equation which is coupled to a transient storage model using an exponential decay term. The model parameters: longitudinal dispersion ( D L ), exchange coefficient ( α ), main channel area ( A ) and storage zone ( A s ) are estimated based on field experiments conducted in a confined, bedrock‐gravel bed river with pool‐riffle morphology located in coastal northern California. The riparian zone is inhabited by Alnus rhombifolia that disperse buoyant seeds in mid‐spring coinciding with the end of the wet, Mediterranean season. Artificial seeds, with similar traits of buoyancy and density to alder seeds, were used to quantify transport times and depositional locations. Preferential deposition resulted in stream reaches with larger A s , high A s / A ratios, and faster exchange coefficients corresponding to divergent stream flow (back‐eddies, re‐circulating flow, flow expansions) caused by geomorphic structures such as the ends of bar/riffle features and bends in the stream. The results demonstrate the importance of transient storage for seed transport and depositional processes. Morphological features that increase a channel's complexity create complex flow structures that detain seeds and provide a greater opportunity for deposition to occur. The model provides a simplification of river hydraulics to represent dispersal dynamics and lends itself to further understanding of hydrochory processes and associated population structure. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".