Linking populations to landscapes: richness scenarios resulting from changes in the dynamics of an ecosystem engineer
Bibliographic record
Abstract
Predicting the effects of the loss of individual species on diversity represents one of the primary challenges facing community ecology. One pathway by which organisms of one species affect the distribution of species is ecosystem engineering. Changes in the dynamics of ecosystem engineers that lead to changes in the distribution of the patches of altered habitat are likely to lead to changes in diversity. I link data on the distribution of plant species found in the riparian zone of the Adirondacks (New York, USA) in patches modified by beaver and in unmodified forest patches to a model connecting the dynamics of ecosystem engineers to the dynamics of the patches that they create. These analyses demonstrate that changes in key parameters of the model, such as decreases in beaver colonization rates and rate of patch abandonment, lead to changes in species richness of up to 45% at the landscape scale, and that these changes are likely to occur over long time scales. This general approach of linking the population dynamics or behavior of a single species to changes in species richness at the landscape scale provides a means for both testing the importance of ecosystem engineering in different systems and developing scenarios to predict how changes in the dynamics of a single species are likely to affect species richness.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".