Predicting Plant Diversity Response to Disturbance: Applicability of the Intermediate Disturbance Hypothesis and Mass Ratio Hypothesis
Bibliographic record
Abstract
Predicting the relationships between disturbance, biodiversity and productivity of ecosystems continue to preoccupy ecologists and resource managers. Two hypotheses underpin many of the discussions. The Intermediate Disturbance Hypothesis (IDH), which proposes that biodiversity peaks at intermediate levels of disturbance, is often extended to predict that productivity follows the same response pattern. The Mass Ratio Hypothesis (MRH) proposes that the biological traits of the dominant species are the critical drivers of ecosystem function (e.g., productivity) and that these species increase in biomass rapidly after disturbance then stabilize. As a consequence, species diversity first peaks then declines after disturbance as a few species dominate the site. Both provide a conceptual link among disturbance, species diversity and productivity (an index of ecosystem function). We assessed the current state of empirical support for these two hypotheses with a literature survey and determined if their conformance is related to ecosystem type or site productivity. Conformance of IDH reported in past reviews (considering all ecosystems) ranged from 16 to 21%. This contrasts with our finding that in terrestrial ecosystems conformance to IDH was 46% (22 of 48 studies), 17% studies reported non-compliance, and 23% reported inconclusive results. Most studies explained their results with respect to IDH or MRH. Only two studies were specifically designed to test the validity of IDH or MRH. We conclude that (i) the IDH is mostly applicable to predict species diversity response to disturbance in upland sites of medium to high productivity and the MRH is applicable to organic sites of low productivity; (ii) there is a critical need for more studies specifically designed to test these hypotheses in natural ecosystems using common protocols; and (iii) enhanced understanding of these models will add value in refining management policies and in the selection of meaningful diversity indicators of sustainability.
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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".