Species Persistence and Species Occurrence are Decoupled across an Environmental Stress-Gradient
Bibliographic record
Abstract
<div class="page" title="Page 5"> <div class="section"> <div class="layoutArea"> <div class="column"> <span style="font-size: 12pt; font-family: TimesNewRomanPSMT;">Biological diversity is well known to depend on environmental productivity gradients. However, while measures of ecological diversity predominantly focus on species occurrence, they often fail to account for what percentage of the species actually persist, i.e., those which exhibit positive growth rates. Species persistence may change across environmental gradients via three processes: abiotic filtering, species interactions, and dispersal into habitats where they can persist. Here, we present an empirical investigation into (i) how persistence changes across a gradient from high-stress and low productivity to more benign, high productivity environments, (ii) how the type of species interaction changes across across this gradient, and (iii) how dispersal fuels differences between persistence and occurrence. We measured invasion growth rates (i.e., persistence) of four annual plant species in serpentine grasslands of McLaughlin Nature Reserve, California USA, and compared this to each species' natural distribution (i.e., occurrence). In contrast to our expectations, we found that species differed drastically in their patterns of occurrence versus persistence along a productivity gradient, depending on their biotic interactions, environmental suitability, and their ability to disperse. We found significant mismatches between species’ occurrence versus persistence across the gradient, where occurrence was higher than persistence in productive conditions, and lower than persistence in harsh conditions. This outcome could lead to an over- or underestimation of species’ persistence depending on abiotic conditions, which in turn leads us to overlook possible threats of species extirpation. Second, we found support for the stress-gradient hypothesis, where higher environmental stress leads to increased trends of facilitation and lower environmental stress leads to increased competition in persisting species. Interestingly, dispersal more commonly increased the proportion of sink populations in less stressful environments, whereas the proportion of dispersal limited populations decreased in less stressful environments. These results highlight the importance of including persistence as a measure of biological diversity, as dispersal dynamics and biotic interactions may be fueling large mismatches between diversity versus persistence across productivity gradients.</span> </div> </div> </div> </div>
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".