Using species combinations in indicator value analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Indicator species are often determined using an analysis of the relationship between the species occurrence or abundance values from a set of sites and the classification of the same sites into site groups (habitat types, community types, disturbance states, etc.). It may happen, however, that a particular site group has no indicator species even if its sites have a community composition that is clearly distinct from the sites of other site groups. This motivates an exploration of the indicator value of not only individual species but also species combinations. Here, we present a novel statistical approach to determine indicators of site groups using species data. Unlike traditional indicator value analysis, we allow indicators to be species combinations in addition to single species. We require that all the species forming the combination must occur in the site to use the combination as an indicator. We present a simple algorithm that identifies the set of indicators (each one being either a single species or a species combination) that show high positive predictive value for the target site group. Moreover, we demonstrate the use of the percentage of sites of the site group where at least one of its valid indicators occurs to determine whether the group can be reliably predicted throughout its range. Using a simulation study, we show that if two species are not strongly correlated and their frequency in the data set is larger than the frequency of sites belonging to the site group, the joint occurrence of the two species has higher positive predictive value for the site group than the two species taken independently. We illustrate the proposed method by determining which combinations of vascular plants can be used as indicators for 29 shrubland and forest vegetation types of New Zealand. The proposed methodology extends traditional indicator value analyses and will be useful to develop multispecies ecological or environmental indicators. Further, it will allow newly surveyed sites to be reliably assigned to previously defined vegetation types.
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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 | 0.001 | 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 | 0.000 | 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 it