Assessing the persistence capacity of communities facing natural disturbances on the basis of species response traits
Bibliographic record
Abstract
Adequately assessing the ecosystem resilience and resistance is a challenging and essential question in the current context of widespread environmental change. Here we suggest the use of a quantitative measure we call Persistence Index (PI) to assess the capacity of communities to maintain their functions and services after disturbances. First, we present the formulation of PI that is based on the diversity, abundance, and redundancy of disturbance- and taxon-specific response traits. Then, we use simulated data sets to study the effects of species richness and the number and frequency of traits on PI values. Finally, we illustrate our approach by assessing the persistence capacity of forest communities in Peninsular Spain and the Balearic Islands in response to fire, drought and windstorm events. The Persistence Index was found to be relatively independent on the number of considered traits, but variable according to the frequency of traits in the community. In the evaluation made with national forest inventory data, PI was found to vary within and among different forest types, being particularly high in stands dominated by non-native species (e.g. Eucalyptus sp.) or in mixed-stands composed by evergreen and deciduous broadleaf species. We also found PI values to increase with the number of species present in the stand, although this relationship saturated due to overlap in species response traits. The presented index is complementary to other approaches developed to study the functional structure of communities through the distribution of species in a functional space. It can be applied to a broad spectrum of communities subjected to different types of stressors, making it a useful tool to guide ecosystem management decisions in a context of changing climate and uncertain disturbance regimes.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".